------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  W:\dofiles\replication\Replication_log.log
  log type:  text
 opened on:   5 Jun 2024, 06:26:22

. do "$home\dofiles\Setup_data.do"

. ****************************************************************************************************************
> ****
. *****Do-file for Setuping data
. *****Who is mobilized to vote by short text messages? Evidence from a nationwide field experiment with young vot
> ers
. ****************************************************************************************************************
> ****
. *****Last edited 24/6/5
. 
. 
. *Set seed
. set seed 052212145

. 
. *edited 27/6/2022
. 
. 
. 
. 
. cd "W:\"
W:\

. 
. 
. *Save admin data variables
. use "D:\f13\custom-made\folk_aineistot\u1761_folk_perus.dta", clear

. keep if vuosi==2019
(21,406,048 observations deleted)

. drop if shnro==""
(0 observations deleted)

. save "data\folk2019.dta", replace
file data\folk2019.dta saved

. 
. *Save treatment variables
. use "D:\f13\external\u1761_treatment_missing.dta", clear

. drop if shnro==""
(30 observations deleted)

. save "data\treatment.dta", replace
file data\treatment.dta saved

. 
. *Save voting data variables
. use "D:\f13\custom-made\vaaliaineistot\u1761_kvaalit_2021.dta"

. drop if shnro==""
(140 observations deleted)

. gen voted21=atyyppi!=""

. gen voted21adv=atyyppi!="E" & atyyppi!="" 

. drop atyyppi

. save "data\election2021.dta", replace
file data\election2021.dta saved

. 
. 
. 
. use "D:\f13\custom-made\vaaliaineistot\u1761_ava_2022.dta", clear

. drop if shnro==""
(63 observations deleted)

. gen voted22=atyyppi!=""

. gen voted22elday=atyyppi=="E"

. gen voted22adv=atyyppi!="E" & atyyppi!="" 

. drop atyyppi

. 
. 
. *Merge all data
. merge 1:1 shnro using "data\treatment.dta"

    Result                      Number of obs
    -----------------------------------------
    Not matched                     1,756,607
        from master                 1,753,173  (_merge==1)
        from using                      3,434  (_merge==2)

    Matched                           277,182  (_merge==3)
    -----------------------------------------

. 
. 
. merge 1:1 shnro using "data\folk2019.dta", gen(_mergefolk)

    Result                      Number of obs
    -----------------------------------------
    Not matched                     1,550,839
        from master                    11,023  (_mergefolk==1)
        from using                  1,539,816  (_mergefolk==2)

    Matched                         2,022,766  (_mergefolk==3)
    -----------------------------------------

. 
. 
. 
. 
. 
. merge 1:1 shnro using "data\election2021.dta", gen(_el21)

    Result                      Number of obs
    -----------------------------------------
    Not matched                     1,621,269
        from master                 1,620,590  (_el21==1)
        from using                        679  (_el21==2)

    Matched                         1,953,015  (_el21==3)
    -----------------------------------------

. 
. *Generate control and outcome variables
. destring kunta, replace
kunta: all characters numeric; replaced as int
(11702 missing values generated)

. gen treated=treatment
(3,523,183 missing values generated)

. recode treated (2/3=1)
(20438 changes made to treated)

. gen treated1=treatment
(3,523,183 missing values generated)

. recode treated1 (2/3=.)
(20438 changes made to treated1)

. gen treated2=treatment
(3,523,183 missing values generated)

. recode treated2 (1=.) (2=1) (3=.)
(30661 changes made to treated2)

. gen treated3=treatment
(3,523,183 missing values generated)

. recode treated3 (1=.) (2=.) (3=1)
(30661 changes made to treated3)

. 
. destring yotutk, gen(highschool)
yotutk: all characters numeric; highschool generated as byte
(617285 missing values generated)

. recode highschool (.=0) (4=1)
(1600326 changes made to highschool)

. 
. destring sose, gen(ses)
sose: all characters numeric; ses generated as byte
(11702 missing values generated)

. gen ses1d=real(substr(sose,1,1)) 
(11,702 missing values generated)

. gen female=sukup=="2"

. gen lincome=ln(svatva_k)
(593,223 missing values generated)

. rename ika age

. rename kunta kunta19

. 
. gen firstvote=0

. replace firstvote=1 if syntyv==2003 & voted21==. & treated!=.
(1,928 real changes made)

. gen foreign=syntyp2
(11,702 missing values generated)

. destring foreign, replace
foreign: all characters numeric; replaced as byte
(11702 missing values generated)

. recode foreign (11/12=0) (21/22=1)
(3562582 changes made to foreign)

. 
. 
. 
. 
. 
. *Merge household id for 2014
. merge 1:1 shnro using "data\folkperhe2014.dta", gen(_f14)

    Result                      Number of obs
    -----------------------------------------
    Not matched                     1,187,853
        from master                 1,111,967  (_f14==1)
        from using                     75,886  (_f14==2)

    Matched                         2,462,317  (_f14==3)
    -----------------------------------------

. drop if _f14==2
(75,886 observations deleted)

. 
. 
. 
. *Identify mother based on household id in 2014, age difference and number of children in the household
. bys petu: egen mmage=max(age) if female==1 & petu!=""
(2,322,697 missing values generated)

. 
. bys petu: egen mage=mean(mmage)
(1,198,519 missing values generated)

. 
. gen diffage=mage-age
(1,199,570 missing values generated)

. 
. 
. *Generate controls using mother's data if available otherwise youth's own data
. gen mmses1d=ses1d if age==mage & female==1
(2,698,959 missing values generated)

. bys petu: egen mses1d=mean(mmses1d)
(1,198,519 missing values generated)

. replace mses1d=. if a25lkm==0 | a25lkm==. | diffage<16 | age>25
(1,654,176 real changes made, 1,654,176 to missing)

. 
. gen moses1d=mses1d
(2,852,695 missing values generated)

. replace moses1d=ses1d if moses1d==.
(2,840,993 real changes made)

. 
. gen mmhighschool=highschool if age==mage & female==1
(2,698,959 missing values generated)

. bys petu: egen mhighschool=mean(mmhighschool)
(1,198,519 missing values generated)

. replace mhighschool=. if a25lkm==0 | a25lkm==. | diffage<16 | age>24
(1,674,143 real changes made, 1,674,143 to missing)

. 
. gen mohighschool=mhighschool
(2,872,662 missing values generated)

. replace mohighschool=highschool if mohighschool==.
(2,872,662 real changes made)

. replace mohighschool=ceil(mohighschool)
(6 real changes made)

. replace moses1d=ceil(moses1d)
(20 real changes made)

. 
. gen mmlincome=lincome if age==mage & female==1
(2,709,162 missing values generated)

. bys petu: egen mlincome=mean(mmlincome)
(1,226,465 missing values generated)

. replace mlincome=. if a25lkm==0 | a25lkm==. | diffage<16 | age>24
(1,653,450 real changes made, 1,653,450 to missing)

. 
. gen molincome=mlincome
(2,879,915 missing values generated)

. replace molincome=lincome if molincome==.
(2,657,410 real changes made)

. 
. *Merge cohabitant id for 2020
. merge 1:1 shnro using "data\folkperhe2020.dta", gen(_f20)

    Result                      Number of obs
    -----------------------------------------
    Not matched                     1,269,065
        from master                 1,236,347  (_f20==1)
        from using                     32,718  (_f20==2)

    Matched                         2,337,937  (_f20==3)
    -----------------------------------------

. drop if _f20==2
(32,718 observations deleted)

. 
. 
. *Generate spillover treatment if exactly one pontentilly treated in the household
. bys petu20: egen ntreatedf=total(treated) if petu20!=""
(1,236,347 missing values generated)

. bys petu20: egen ncontrolf=total(treatment0) if petu20!=""
(1,236,347 missing values generated)

. gen treatedf=1 if ntreatedf==1 & treated==. & ncontrolf<1
(3,542,553 missing values generated)

. replace treatedf=0 if ncontrolf==1 & treated==. & ntreatedf<1
(20,941 real changes made)

. 
. 
. 
. bys petu: egen ntreatedf14=total(treated) if petu!=""
(1,111,967 missing values generated)

. bys petu: egen ncontrolf14=total(treatment0) if petu!=""
(1,111,967 missing values generated)

. gen treatedf14=1 if ntreatedf14==1 & treated==. & ncontrolf14<1
(3,513,190 missing values generated)

. replace treatedf14=0 if ncontrolf14==1 & treated==. & ntreatedf14<1
(40,389 real changes made)

. 
. 
. bys petu20: egen ntreatedf1=total(treated1) if petu20!=""
(1,236,347 missing values generated)

. bys petu20: egen ntreatedf2=total(treated2) if petu20!=""
(1,236,347 missing values generated)

. bys petu20: egen ntreatedf3=total(treated3) if petu20!=""
(1,236,347 missing values generated)

. 
. 
. 
. gen treatedf1=1 if ntreatedf1==1 & treated==. & ncontrolf<1
(3,563,049 missing values generated)

. replace treatedf1=0 if ncontrolf==1 & treated==. & ntreatedf<1
(20,941 real changes made)

. 
. gen treatedf2=1 if ntreatedf2==1 & treated==. & ncontrolf<1
(3,563,011 missing values generated)

. replace treatedf2=0 if ncontrolf==1 & treated==. & ntreatedf<1
(20,941 real changes made)

. 
. gen treatedf3=1 if ntreatedf3==1 & treated==. & ncontrolf<1
(3,563,145 missing values generated)

. replace treatedf3=0 if ncontrolf==1 & treated==. & ntreatedf<1
(20,941 real changes made)

. 
. 
. 
. 
. *Generate predicted voting (logit)
. logit voted22 molincome female firstvote foreign i.moses1d i.mohighschool i.kunta19 if  treated==0

note: 18.kunta19 != 0 predicts success perfectly;
      18.kunta19 omitted and 1 obs not used.

note: 52.kunta19 != 0 predicts failure perfectly;
      52.kunta19 omitted and 1 obs not used.

note: 78.kunta19 != 0 predicts failure perfectly;
      78.kunta19 omitted and 3 obs not used.

note: 82.kunta19 != 0 predicts failure perfectly;
      82.kunta19 omitted and 2 obs not used.

note: 90.kunta19 != 0 predicts failure perfectly;
      90.kunta19 omitted and 3 obs not used.

note: 105.kunta19 != 0 predicts failure perfectly;
      105.kunta19 omitted and 2 obs not used.

note: 148.kunta19 != 0 predicts failure perfectly;
      148.kunta19 omitted and 6 obs not used.

note: 152.kunta19 != 0 predicts failure perfectly;
      152.kunta19 omitted and 1 obs not used.

note: 172.kunta19 != 0 predicts failure perfectly;
      172.kunta19 omitted and 4 obs not used.

note: 181.kunta19 != 0 predicts failure perfectly;
      181.kunta19 omitted and 1 obs not used.

note: 204.kunta19 != 0 predicts success perfectly;
      204.kunta19 omitted and 1 obs not used.

note: 213.kunta19 != 0 predicts failure perfectly;
      213.kunta19 omitted and 1 obs not used.

note: 218.kunta19 != 0 predicts failure perfectly;
      218.kunta19 omitted and 1 obs not used.

note: 235.kunta19 != 0 predicts failure perfectly;
      235.kunta19 omitted and 1 obs not used.

note: 250.kunta19 != 0 predicts failure perfectly;
      250.kunta19 omitted and 2 obs not used.

note: 273.kunta19 != 0 predicts failure perfectly;
      273.kunta19 omitted and 2 obs not used.

note: 275.kunta19 != 0 predicts failure perfectly;
      275.kunta19 omitted and 1 obs not used.

note: 284.kunta19 != 0 predicts failure perfectly;
      284.kunta19 omitted and 1 obs not used.

note: 291.kunta19 != 0 predicts success perfectly;
      291.kunta19 omitted and 2 obs not used.

note: 312.kunta19 != 0 predicts failure perfectly;
      312.kunta19 omitted and 3 obs not used.

note: 320.kunta19 != 0 predicts failure perfectly;
      320.kunta19 omitted and 1 obs not used.

note: 407.kunta19 != 0 predicts failure perfectly;
      407.kunta19 omitted and 1 obs not used.

note: 580.kunta19 != 0 predicts success perfectly;
      580.kunta19 omitted and 1 obs not used.

note: 631.kunta19 != 0 predicts failure perfectly;
      631.kunta19 omitted and 1 obs not used.

note: 636.kunta19 != 0 predicts failure perfectly;
      636.kunta19 omitted and 3 obs not used.

note: 681.kunta19 != 0 predicts failure perfectly;
      681.kunta19 omitted and 1 obs not used.

note: 738.kunta19 != 0 predicts failure perfectly;
      738.kunta19 omitted and 1 obs not used.

note: 742.kunta19 != 0 predicts failure perfectly;
      742.kunta19 omitted and 1 obs not used.

note: 751.kunta19 != 0 predicts failure perfectly;
      751.kunta19 omitted and 1 obs not used.

note: 755.kunta19 != 0 predicts failure perfectly;
      755.kunta19 omitted and 3 obs not used.

note: 834.kunta19 != 0 predicts failure perfectly;
      834.kunta19 omitted and 2 obs not used.

note: 846.kunta19 != 0 predicts failure perfectly;
      846.kunta19 omitted and 2 obs not used.

note: 854.kunta19 != 0 predicts failure perfectly;
      854.kunta19 omitted and 3 obs not used.

note: 892.kunta19 != 0 predicts failure perfectly;
      892.kunta19 omitted and 2 obs not used.

note: 918.kunta19 != 0 predicts failure perfectly;
      918.kunta19 omitted and 1 obs not used.

note: 922.kunta19 != 0 predicts failure perfectly;
      922.kunta19 omitted and 3 obs not used.

note: 976.kunta19 != 0 predicts failure perfectly;
      976.kunta19 omitted and 4 obs not used.

Iteration 0:   log likelihood = -12251.102  
Iteration 1:   log likelihood = -11448.359  
Iteration 2:   log likelihood = -11429.424  
Iteration 3:   log likelihood = -11429.341  
Iteration 4:   log likelihood = -11429.341  

Logistic regression                                    Number of obs =  19,809
                                                       LR chi2(243)  = 1643.52
                                                       Prob > chi2   =  0.0000
Log likelihood = -11429.341                            Pseudo R2     =  0.0671

--------------------------------------------------------------------------------
       voted22 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     molincome |   .0242145     .02524     0.96   0.337     -.025255     .073684
        female |   .5120396   .0327249    15.65   0.000        .4479    .5761791
     firstvote |   .4605732   .0814907     5.65   0.000     .3008544     .620292
       foreign |  -.8500771   .1153582    -7.37   0.000    -1.076175   -.6239793
               |
       moses1d |
            2  |  -.4533362   .1394158    -3.25   0.001     -.726586   -.1800863
            3  |  -.2742606   .1306086    -2.10   0.036    -.5302487   -.0182725
            4  |  -.5720635   .1257615    -4.55   0.000    -.8185515   -.3255755
            5  |  -.7917894   .1297612    -6.10   0.000    -1.046117   -.5374621
            6  |  -.4849183   .1418811    -3.42   0.001    -.7630001   -.2068365
            7  |  -.4116372   .1467247    -2.81   0.005    -.6992124   -.1240621
            8  |  -.7742019   .1411003    -5.49   0.000    -1.050753   -.4976504
            9  |  -.8724921   .1728386    -5.05   0.000    -1.211249   -.5337346
               |
1.mohighschool |   .6389546   .0352251    18.14   0.000     .5699148    .7079944
               |
       kunta19 |
            9  |   1.674947   .4115527     4.07   0.000     .8683182    2.481575
           10  |   .2838539   .3020563     0.94   0.347    -.3081655    .8758733
           16  |   1.213521   1.434742     0.85   0.398    -1.598521    4.025564
           18  |          0  (empty)
           19  |  -.3200516   .5722083    -0.56   0.576    -1.441559    .8014561
           20  |  -.8470523   .8106608    -1.04   0.296    -2.435918    .7418136
           46  |   1.044773   1.437322     0.73   0.467    -1.772327    3.861872
           49  |   .4407869   .2948905     1.49   0.135    -.1371879    1.018762
           50  |   1.903571   .9131276     2.08   0.037     .1138736    3.693268
           51  |   -.011715   .3655581    -0.03   0.974    -.7281957    .7047657
           52  |          0  (empty)
           61  |   .3747354   .6613408     0.57   0.571    -.9214688     1.67094
           69  |     .33371   .3350081     1.00   0.319    -.3228939    .9903139
           71  |   .3198452   .3210598     1.00   0.319    -.3094205     .949111
           72  |    .417341   .9462008     0.44   0.659    -1.437178     2.27186
           74  |   .4876809    .674267     0.72   0.470    -.8338583     1.80922
           75  |   .8022258    1.05238     0.76   0.446    -1.260402    2.864853
           77  |   -.030929   .5047306    -0.06   0.951    -1.020183    .9583248
           78  |          0  (empty)
           79  |  -.5621361   .4770264    -1.18   0.239    -1.497091    .3728184
           82  |          0  (empty)
           86  |   .3519933   .4163142     0.85   0.398    -.4639675    1.167954
           90  |          0  (empty)
           91  |   .5550735   .2549487     2.18   0.029     .0553833    1.054764
           92  |    .000305   .2354028     0.00   0.999    -.4610759    .4616859
           97  |    1.15344    1.47005     0.78   0.433    -1.727806    4.034686
           98  |  -.6348565    1.15435    -0.55   0.582    -2.897342    1.627629
           99  |   .2063394    1.29237     0.16   0.873    -2.326659    2.739338
          102  |   -.470691   1.152471    -0.41   0.683    -2.729493    1.788111
          105  |          0  (empty)
          106  |   .0999517   .4250892     0.24   0.814    -.7332079    .9331112
          108  |   .0447786   .3561284     0.13   0.900    -.6532203    .7427774
          109  |  -.2277804   .3835965    -0.59   0.553    -.9796157    .5240549
          111  |  -.7501518   .8162595    -0.92   0.358    -2.349991    .8496874
          139  |   .5480789   .6531077     0.84   0.401    -.7319887    1.828146
          140  |   -.357967    .619409    -0.58   0.563    -1.571986    .8560524
          143  |   1.317249   .6875727     1.92   0.055    -.0303682    2.664867
          145  |  -.5526185   1.133158    -0.49   0.626    -2.773567     1.66833
          146  |  -.3457661   .6077097    -0.57   0.569    -1.536855    .8453231
          148  |          0  (empty)
          151  |   1.452191   .5325626     2.73   0.006     .4083877    2.495995
          152  |          0  (empty)
          153  |   1.296583   .7562656     1.71   0.086    -.1856704    2.778836
          165  |   -.291108   .3464292    -0.84   0.401    -.9700967    .3878807
          167  |   .1711606   .2388967     0.72   0.474    -.2970683    .6393896
          169  |   .6062594   .4217112     1.44   0.151    -.2202794    1.432798
          171  |   .2308445   1.179962     0.20   0.845    -2.081839    2.543528
          172  |          0  (empty)
          176  |   .1563518   1.245219     0.13   0.900    -2.284233    2.596936
          177  |  -1.700508   1.073932    -1.58   0.113    -3.805377    .4043603
          178  |   1.074396    1.44422     0.74   0.457    -1.756223    3.905015
          179  |   .3336795   .2337251     1.43   0.153    -.1244133    .7917723
          181  |          0  (empty)
          182  |   .3001184   .2901055     1.03   0.301    -.2684779    .8687148
          186  |   .0865896   .5090677     0.17   0.865    -.9111648    1.084344
          202  |   .3393746   .2824544     1.20   0.230    -.2142258    .8929749
          204  |          0  (empty)
          205  |  -.1706411   .4165037    -0.41   0.682    -.9869734    .6456912
          208  |  -.1587895   .8454903    -0.19   0.851     -1.81592    1.498341
          211  |  -.3203105   .4876991    -0.66   0.511    -1.276183    .6355621
          213  |          0  (empty)
          214  |  -.1193179   .7157682    -0.17   0.868    -1.522198    1.283562
          216  |   .3902774   .7360855     0.53   0.596    -1.052424    1.832979
          217  |   .5069567   .3636463     1.39   0.163    -.2057769     1.21969
          218  |          0  (empty)
          224  |   .2744506   .3711239     0.74   0.460    -.4529389     1.00184
          226  |   .3406583   .4483029     0.76   0.447    -.5379993    1.219316
          230  |   1.462656   .5016151     2.92   0.004     .4795081    2.445803
          231  |  -.4701263   1.166584    -0.40   0.687     -2.75659    1.816337
          232  |  -.8724111   1.106577    -0.79   0.430    -3.041261    1.296439
          233  |   .6554104    .759708     0.86   0.388      -.83359    2.144411
          235  |          0  (empty)
          236  |   .5897855   .3987151     1.48   0.139    -.1916818    1.371253
          239  |  -.0781813    .695082    -0.11   0.910    -1.440517    1.284154
          240  |   .3377066   .2950141     1.14   0.252    -.2405104    .9159236
          241  |  -1.158441   1.101713    -1.05   0.293    -3.317758    1.000876
          244  |   .2889509   .2953551     0.98   0.328    -.2899345    .8678362
          245  |   .1453119   .2673734     0.54   0.587    -.3787303     .669354
          249  |  -.0658633   .8498363    -0.08   0.938    -1.731512    1.599785
          250  |          0  (empty)
          257  |  -.0750575   .2876795    -0.26   0.794     -.638899     .488784
          260  |  -.1299745   .8573453    -0.15   0.880     -1.81034    1.550391
          261  |    .900109   .7032248     1.28   0.201    -.4781863    2.278404
          263  |   -.458795   1.142673    -0.40   0.688    -2.698393    1.780803
          265  |   .4058303   .8835184     0.46   0.646    -1.325834    2.137495
          271  |  -.1124957   1.261663    -0.09   0.929    -2.585311    2.360319
          272  |   .1855507    .393731     0.47   0.637     -.586148    .9572494
          273  |          0  (empty)
          275  |          0  (empty)
          276  |   .2850074     .29863     0.95   0.340    -.3002966    .8703113
          280  |   1.695812   1.269417     1.34   0.182    -.7921984    4.183823
          284  |          0  (empty)
          285  |   -.245756   .5726784    -0.43   0.668    -1.368185     .876673
          286  |   .1782029   .4620868     0.39   0.700    -.7274705    1.083876
          287  |   .5595896    .378061     1.48   0.139    -.1813964    1.300576
          288  |   1.413059   1.433816     0.99   0.324    -1.397168    4.223287
          290  |   1.876221   1.282493     1.46   0.143    -.6374198    4.389861
          291  |          0  (empty)
          297  |   .4738863   .3028114     1.56   0.118    -.1196132    1.067386
          300  |  -.2202707   .4993268    -0.44   0.659    -1.198933    .7583919
          301  |   .0640886   1.149254     0.06   0.956    -2.188408    2.316585
          304  |   1.403118   1.465586     0.96   0.338    -1.469377    4.275613
          305  |  -.3101376     .34466    -0.90   0.368    -.9856589    .3653837
          309  |  -.6704336   .5322627    -1.26   0.208    -1.713649    .3727821
          312  |          0  (empty)
          317  |   .6545864   1.282174     0.51   0.610    -1.858429    3.167601
          320  |          0  (empty)
          322  |   .2543428   .4037759     0.63   0.529    -.5370435    1.045729
          398  |  -.0938301   .3737412    -0.25   0.802    -.8263495    .6386893
          399  |  -.2600411   1.198643    -0.22   0.828    -2.609339    2.089256
          400  |  -.9948457   1.094901    -0.91   0.364    -3.140813    1.151121
          402  |   .2122163   .3553518     0.60   0.550    -.4842604    .9086929
          403  |   .0799025   .5975903     0.13   0.894    -1.091353    1.251158
          405  |   .2939945   .3804941     0.77   0.440    -.4517602    1.039749
          407  |          0  (empty)
          408  |    .023667   .7324771     0.03   0.974    -1.411962    1.459296
          410  |   .2299252   .3101088     0.74   0.458    -.3778768    .8377273
          418  |   .7574841    .465766     1.63   0.104    -.1554005    1.670369
          420  |   .9618118   1.254721     0.77   0.443    -1.497397     3.42102
          422  |  -.2450898   .3802941    -0.64   0.519    -.9904526     .500273
          423  |   1.514367   .7878446     1.92   0.055    -.0297795    3.058514
          425  |   .8681462   .3141693     2.76   0.006     .2523856    1.483907
          426  |  -.4600943   .3684057    -1.25   0.212    -1.182156    .2619675
          430  |   1.309746   .8235298     1.59   0.112    -.3043426    2.923835
          433  |  -.0555129    1.13137    -0.05   0.961    -2.272958    2.161932
          434  |  -.4497946   1.133212    -0.40   0.691     -2.67085    1.771261
          436  |   .3605219   .5577196     0.65   0.518    -.7325884    1.453632
          441  |    .910645    .457254     1.99   0.046     .0144436    1.806846
          444  |  -.1381642    .265442    -0.52   0.603    -.6584211    .3820926
          445  |   .2200161   .3314005     0.66   0.507    -.4295169    .8695491
          475  |    .773753   .3513333     2.20   0.028     .0851524    1.462354
          481  |    .258301   .3986823     0.65   0.517    -.5231019    1.039704
          483  |   2.146137   1.261827     1.70   0.089     -.326998    4.619271
          484  |  -.6879947   1.150772    -0.60   0.550    -2.943466    1.567477
          489  |   1.039917   1.490622     0.70   0.485    -1.881649    3.961483
          491  |   .2725938   .4526589     0.60   0.547    -.6146014    1.159789
          494  |   .7371373    .360075     2.05   0.041     .0314033    1.442871
          498  |    .570908   1.253086     0.46   0.649    -1.885096    3.026912
          499  |   .4087372   .5826069     0.70   0.483    -.7331513    1.550626
          500  |   .6198844   .3454285     1.79   0.073     -.057143    1.296912
          503  |  -.3553349   .4383037    -0.81   0.418    -1.214394    .5037246
          505  |  -.0487857   .6435937    -0.08   0.940    -1.310206    1.212635
          507  |   .1940738   .4347804     0.45   0.655    -.6580801    1.046228
          508  |   .0534529   .6493116     0.08   0.934    -1.219175     1.32608
          529  |  -.2690478   .7005195    -0.38   0.701    -1.642041    1.103945
          531  |  -.8625584   .5373371    -1.61   0.108     -1.91572    .1906029
          535  |   .3650422   .2945602     1.24   0.215    -.2122852    .9423695
          536  |  -.1587769   .5145283    -0.31   0.758    -1.167234    .8496801
          541  |  -.9092578   1.123873    -0.81   0.418    -3.112009    1.293493
          543  |   .0902428   .2621297     0.34   0.731     -.423522    .6040076
          545  |   1.253145    .696053     1.80   0.072    -.1110936    2.617384
          560  |  -.3449803   1.167859    -0.30   0.768    -2.633941    1.943981
          562  |   .2354735    .750205     0.31   0.754    -1.234901    1.705848
          563  |  -.1062495   .7260255    -0.15   0.884    -1.529233    1.316734
          564  |   .4179639   .2321342     1.80   0.072    -.0370107    .8729386
          577  |  -.1477873   .3636369    -0.41   0.684    -.8605026    .5649279
          578  |    .541628   .5102659     1.06   0.288    -.4584748    1.541731
          580  |          0  (empty)
          581  |   .6138313   .3891272     1.58   0.115     -.148844    1.376507
          583  |   .4937483   1.290642     0.38   0.702    -2.035864    3.023361
          584  |    1.52659    .403106     3.79   0.000     .7365164    2.316663
          588  |   .7229933   .7912272     0.91   0.361    -.8277836     2.27377
          592  |   .4700165   .4693758     1.00   0.317    -.4499431    1.389976
          593  |   -.998012   .3833318    -2.60   0.009    -1.749329   -.2466954
          595  |   .1430782   1.333292     0.11   0.915    -2.470125    2.756282
          598  |    .550182   .7078932     0.78   0.437    -.8372632    1.937627
          599  |   .1202344   .8895039     0.14   0.892    -1.623161     1.86363
          601  |   1.493831   .9731295     1.54   0.125    -.4134679     3.40113
          604  |  -.3562466   .4925529    -0.72   0.470    -1.321633    .6091393
          607  |   .0071313   .5270876     0.01   0.989    -1.025941    1.040204
          608  |   .8029343   .5899035     1.36   0.173    -.3532554    1.959124
          609  |   .2328056    .245371     0.95   0.343    -.2481128     .713724
          611  |   .7293533   1.257141     0.58   0.562    -1.734597    3.193304
          614  |  -.4548527   .6853394    -0.66   0.507    -1.798093    .8883879
          615  |  -.3691694   .8315438    -0.44   0.657    -1.998965    1.260626
          620  |  -.9480646   1.093933    -0.87   0.386    -3.092134    1.196005
          623  |   .0427272   .6558498     0.07   0.948    -1.242715    1.328169
          624  |  -.2896263   .5126507    -0.56   0.572    -1.294403    .7151506
          625  |  -.3453332   1.196589    -0.29   0.773    -2.690605    1.999939
          626  |  -.1121309   .4911559    -0.23   0.819    -1.074779     .850517
          630  |   1.912797   1.317832     1.45   0.147    -.6701059    4.495701
          631  |          0  (empty)
          635  |   .5645695   .4032202     1.40   0.161    -.2257276    1.354867
          636  |          0  (empty)
          638  |  -.1786408   .2792628    -0.64   0.522    -.7259858    .3687042
          678  |  -.0378428   .5244692    -0.07   0.942    -1.065784    .9900981
          680  |  -.4707799    .327637    -1.44   0.151    -1.112937    .1713768
          681  |          0  (empty)
          683  |   1.265491   .4113576     3.08   0.002     .4592445    2.071737
          684  |   .0169115   .5075709     0.03   0.973    -.9779093    1.011732
          686  |   1.517934   1.267976     1.20   0.231    -.9672546    4.003122
          687  |   .6731155   .7495656     0.90   0.369    -.7960061    2.142237
          689  |      1.216   1.435233     0.85   0.397    -1.597005    4.029005
          691  |   .4368691   .4495514     0.97   0.331    -.4442356    1.317974
          694  |  -.8207012   .8018165    -1.02   0.306    -2.392233    .7508303
          697  |   .3626435   .9050074     0.40   0.689    -1.411138    2.136426
          698  |   .1301125    .244723     0.53   0.595    -.3495357    .6097608
          702  |   1.360308   1.444645     0.94   0.346    -1.471144     4.19176
          704  |   1.085677   1.434743     0.76   0.449    -1.726368    3.897722
          707  |   1.001726   1.434621     0.70   0.485    -1.810079    3.813531
          710  |   -.334512   .8285922    -0.40   0.686    -1.958523    1.289499
          729  |   .1375403   .3748407     0.37   0.714     -.597134    .8722145
          732  |   .7589446   .9191066     0.83   0.409    -1.042471     2.56036
          734  |   .1343469   .2601813     0.52   0.606    -.3755991    .6442928
          738  |          0  (empty)
          740  |  -.0470894   .5017699    -0.09   0.925     -1.03054    .9363614
          742  |          0  (empty)
          743  |   .5355623   .3416731     1.57   0.117    -.1341046    1.205229
          746  |   .7598465   .3368644     2.26   0.024     .0996044    1.420089
          747  |  -.0105052   .7123239    -0.01   0.988    -1.406634    1.385624
          748  |   1.278574   1.489007     0.86   0.391    -1.639825    4.196973
          749  |   1.682783   .9050614     1.86   0.063     -.091105    3.456671
          751  |          0  (empty)
          753  |  -.1455066   .3151304    -0.46   0.644    -.7631508    .4721377
          755  |          0  (empty)
          758  |   .5551049   .7629218     0.73   0.467    -.9401945    2.050404
          759  |     .22911   .5214061     0.44   0.660    -.7928271    1.251047
          761  |  -.0683211   .3947422    -0.17   0.863    -.8420017    .7053595
          762  |  -.5002639   .6842899    -0.73   0.465    -1.841447    .8409198
          765  |  -.0833826   .3562061    -0.23   0.815    -.7815338    .6147686
          768  |   .8645785   .5627852     1.54   0.124    -.2384603    1.967617
          777  |   1.267216    1.44174     0.88   0.379    -1.558543    4.092975
          778  |  -.1228635   1.183022    -0.10   0.917    -2.441544    2.195817
          781  |   .3751372   .5904272     0.64   0.525    -.7820789    1.532353
          783  |   .8834347   1.528294     0.58   0.563    -2.111967    3.878837
          785  |   .8015567   .6138638     1.31   0.192    -.4015943    2.004708
          790  |   .4796511   .6041482     0.79   0.427    -.7044575     1.66376
          791  |  -.3495075   1.130986    -0.31   0.757      -2.5662    1.867185
          831  |   1.105856   1.432713     0.77   0.440     -1.70221    3.913922
          832  |  -.1276839   1.199099    -0.11   0.915    -2.477875    2.222508
          834  |          0  (empty)
          837  |   .5776229   .2313949     2.50   0.013     .1240972    1.031149
          845  |  -.7346545   .6673088    -1.10   0.271    -2.042556    .5732468
          846  |          0  (empty)
          848  |   .2489325   1.252545     0.20   0.842     -2.20601    2.703875
          849  |   1.176503   .4713051     2.50   0.013      .252762    2.100244
          850  |   1.052326   .6802285     1.55   0.122    -.2808972     2.38555
          851  |    .364769   .5056251     0.72   0.471    -.6262379    1.355776
          853  |   .6085365    .272865     2.23   0.026      .073731    1.143342
          854  |          0  (empty)
          857  |   1.600773   1.432991     1.12   0.264    -1.207837    4.409384
          858  |  -.2909973   .6279163    -0.46   0.643    -1.521691     .939696
          859  |   1.256843   .6324666     1.99   0.047     .0172314    2.496455
          886  |   .0665085   .6209533     0.11   0.915    -1.150538    1.283555
          889  |   .3875162   .5246439     0.74   0.460     -.640767    1.415799
          892  |          0  (empty)
          893  |   .1153928   .8924871     0.13   0.897     -1.63385    1.864635
          895  |  -.0108014   .3134769    -0.03   0.973    -.6252049     .603602
          905  |    .244846   .2435088     1.01   0.315    -.2324226    .7221145
          908  |   -.273284   .3115955    -0.88   0.380        -.884     .337432
          915  |   .6179321   .8131425     0.76   0.447     -.975798    2.211662
          918  |          0  (empty)
          922  |          0  (empty)
          924  |   1.309848   1.032797     1.27   0.205    -.7143969    3.334093
          925  |  -.1219358   .5241424    -0.23   0.816    -1.149236    .9053644
          927  |  -.2386405   .2928085    -0.82   0.415    -.8125346    .3352535
          931  |  -.0954345   .4615714    -0.21   0.836    -1.000098    .8092288
          934  |  -.4535688    1.13922    -0.40   0.691    -2.686398    1.779261
          935  |    .329489    .584704     0.56   0.573    -.8165098    1.475488
          936  |  -.2362122   1.188999    -0.20   0.843    -2.566608    2.094183
          946  |   .9834273   .3263355     3.01   0.003     .3438215    1.623033
          976  |          0  (empty)
          977  |   .4091747   .2833411     1.44   0.149    -.1461637     .964513
          980  |    .688813   .4494802     1.53   0.125     -.192152    1.569778
          981  |  -.5406849   .6920984    -0.78   0.435    -1.897173    .8158032
          989  |   -.933997   .5814292    -1.61   0.108    -2.073577    .2055833
          992  |   .0540622   .5891854     0.09   0.927     -1.10072    1.208844
               |
         _cons |  -1.294797   .3548265    -3.65   0.000    -1.990244   -.5993495
--------------------------------------------------------------------------------

. predict pvote_mother
(option pr assumed; Pr(voted22))
(292,067 missing values generated)

. 
. *Generate predicted voting (enet logit)
. elasticnet logit voted22 molincome female firstvote foreign i.moses1d i.mohighschool i.kunta19 if  treated==0

alpha 1 of 3: alpha = 1
note: 18.kunta19 omitted because it is constant in C.V. subsamples.
note: 52.kunta19 omitted because it is constant in C.V. subsamples.
note: 97.kunta19 omitted because it is constant in C.V. subsamples.
note: 152.kunta19 omitted because it is constant in C.V. subsamples.
note: 181.kunta19 omitted because it is constant in C.V. subsamples.
note: 204.kunta19 omitted because it is constant in C.V. subsamples.
note: 213.kunta19 omitted because it is constant in C.V. subsamples.
note: 218.kunta19 omitted because it is constant in C.V. subsamples.
note: 235.kunta19 omitted because it is constant in C.V. subsamples.
note: 275.kunta19 omitted because it is constant in C.V. subsamples.
note: 284.kunta19 omitted because it is constant in C.V. subsamples.
note: 304.kunta19 omitted because it is constant in C.V. subsamples.
note: 320.kunta19 omitted because it is constant in C.V. subsamples.
note: 407.kunta19 omitted because it is constant in C.V. subsamples.
note: 580.kunta19 omitted because it is constant in C.V. subsamples.
note: 631.kunta19 omitted because it is constant in C.V. subsamples.
note: 681.kunta19 omitted because it is constant in C.V. subsamples.
note: 738.kunta19 omitted because it is constant in C.V. subsamples.
note: 742.kunta19 omitted because it is constant in C.V. subsamples.
note: 751.kunta19 omitted because it is constant in C.V. subsamples.
note: 846.kunta19 omitted because it is constant in C.V. subsamples.
note: 918.kunta19 omitted because it is constant in C.V. subsamples.
10-fold cross-validation with 109 lambdas ...
Grid value 1:     lambda = .1734282   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 2:     lambda = .1580213   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 3:     lambda = .1439831   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 4:     lambda = .1311921   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 5:     lambda = .1195373   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 6:     lambda = .1156188   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 7:     lambda = .1053475   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 8:     lambda = .0959887   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 9:     lambda = .0874614   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235561
Grid value 10:    lambda = .0867141   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235419
Grid value 11:    lambda = .0790106   no. of nonzero coef. =       1
Folds: 1...5....10   CVF = 1.229759
Grid value 12:    lambda = .0719916   no. of nonzero coef. =       1
Folds: 1...5....10   CVF = 1.224807
Grid value 13:    lambda =  .065596   no. of nonzero coef. =       1
Folds: 1...5....10   CVF = 1.220699
Grid value 14:    lambda = .0597687   no. of nonzero coef. =       1
Folds: 1...5....10   CVF =  1.21729
Grid value 15:    lambda =  .054459   no. of nonzero coef. =       1
Folds: 1...5....10   CVF =  1.21445
Grid value 16:    lambda =  .049621   no. of nonzero coef. =       2
Folds: 1...5....10   CVF =  1.21091
Grid value 17:    lambda = .0452128   no. of nonzero coef. =       2
Folds: 1...5....10   CVF =  1.20716
Grid value 18:    lambda = .0411962   no. of nonzero coef. =       2
Folds: 1...5....10   CVF =  1.20404
Grid value 19:    lambda = .0375365   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.201399
Grid value 20:    lambda = .0342018   no. of nonzero coef. =       3
Folds: 1...5....10   CVF =  1.19898
Grid value 21:    lambda = .0311634   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.196682
Grid value 22:    lambda =  .028395   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.194278
Grid value 23:    lambda = .0258724   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.192097
Grid value 24:    lambda =  .023574   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.189837
Grid value 25:    lambda = .0214797   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.187808
Grid value 26:    lambda = .0195715   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.186078
Grid value 27:    lambda = .0178329   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.184581
Grid value 28:    lambda = .0162486   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.183103
Grid value 29:    lambda = .0148052   no. of nonzero coef. =       9
Folds: 1...5....10   CVF =  1.18172
Grid value 30:    lambda = .0134899   no. of nonzero coef. =      10
Folds: 1...5....10   CVF = 1.180455
Grid value 31:    lambda = .0122915   no. of nonzero coef. =      13
Folds: 1...5....10   CVF = 1.179221
Grid value 32:    lambda = .0111996   no. of nonzero coef. =      14
Folds: 1...5....10   CVF =    1.178
Grid value 33:    lambda = .0102046   no. of nonzero coef. =      16
Folds: 1...5....10   CVF =  1.17681
Grid value 34:    lambda = .0092981   no. of nonzero coef. =      19
Folds: 1...5....10   CVF = 1.175732
Grid value 35:    lambda = .0084721   no. of nonzero coef. =      23
Folds: 1...5....10   CVF = 1.174702
Grid value 36:    lambda = .0077194   no. of nonzero coef. =      27
Folds: 1...5....10   CVF = 1.173784
Grid value 37:    lambda = .0070336   no. of nonzero coef. =      33
Folds: 1...5....10   CVF = 1.172917
Grid value 38:    lambda = .0064088   no. of nonzero coef. =      37
Folds: 1...5....10   CVF = 1.172067
Grid value 39:    lambda = .0058395   no. of nonzero coef. =      43
Folds: 1...5....10   CVF =  1.17139
Grid value 40:    lambda = .0053207   no. of nonzero coef. =      47
Folds: 1...5....10   CVF = 1.170886
Grid value 41:    lambda =  .004848   no. of nonzero coef. =      54
Folds: 1...5....10   CVF = 1.170568
Grid value 42:    lambda = .0044173   no. of nonzero coef. =      68
Folds: 1...5....10   CVF = 1.170436
Grid value 43:    lambda = .0040249   no. of nonzero coef. =      78
Folds: 1...5....10   CVF = 1.170462
Grid value 44:    lambda = .0036673   no. of nonzero coef. =      90
Folds: 1...5....10   CVF = 1.170579
Grid value 45:    lambda = .0033416   no. of nonzero coef. =     101
Folds: 1...5....10   CVF = 1.170783
Grid value 46:    lambda = .0030447   no. of nonzero coef. =     113
Folds: 1...5....10   CVF = 1.171069
Grid value 47:    lambda = .0027742   no. of nonzero coef. =     122
Folds: 1...5....10   CVF = 1.171433
Grid value 48:    lambda = .0025278   no. of nonzero coef. =     131
Folds: 1...5....10   CVF = 1.171865
Grid value 49:    lambda = .0023032   no. of nonzero coef. =     141
Folds: 1...5....10   CVF = 1.172376
Grid value 50:    lambda = .0020986   no. of nonzero coef. =     149
Folds: 1...5....10   CVF = 1.172963
... cross-validation complete ... minimum found

alpha 2 of 3: alpha = 0.75
note: 18.kunta19 omitted because it is constant in C.V. subsamples.
note: 52.kunta19 omitted because it is constant in C.V. subsamples.
note: 97.kunta19 omitted because it is constant in C.V. subsamples.
note: 152.kunta19 omitted because it is constant in C.V. subsamples.
note: 181.kunta19 omitted because it is constant in C.V. subsamples.
note: 204.kunta19 omitted because it is constant in C.V. subsamples.
note: 213.kunta19 omitted because it is constant in C.V. subsamples.
note: 218.kunta19 omitted because it is constant in C.V. subsamples.
note: 235.kunta19 omitted because it is constant in C.V. subsamples.
note: 275.kunta19 omitted because it is constant in C.V. subsamples.
note: 284.kunta19 omitted because it is constant in C.V. subsamples.
note: 304.kunta19 omitted because it is constant in C.V. subsamples.
note: 320.kunta19 omitted because it is constant in C.V. subsamples.
note: 407.kunta19 omitted because it is constant in C.V. subsamples.
note: 580.kunta19 omitted because it is constant in C.V. subsamples.
note: 631.kunta19 omitted because it is constant in C.V. subsamples.
note: 681.kunta19 omitted because it is constant in C.V. subsamples.
note: 738.kunta19 omitted because it is constant in C.V. subsamples.
note: 742.kunta19 omitted because it is constant in C.V. subsamples.
note: 751.kunta19 omitted because it is constant in C.V. subsamples.
note: 846.kunta19 omitted because it is constant in C.V. subsamples.
note: 918.kunta19 omitted because it is constant in C.V. subsamples.
10-fold cross-validation with 109 lambdas ...
Grid value 1:     lambda = .1734282   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 2:     lambda = .1580213   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 3:     lambda = .1439831   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 4:     lambda = .1311921   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 5:     lambda = .1195373   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235664
Grid value 6:     lambda = .1156188   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.235434
Grid value 7:     lambda = .1053475   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.230087
Grid value 8:     lambda = .0959887   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.225331
Grid value 9:     lambda = .0874614   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.221329
Grid value 10:    lambda = .0867141   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.220993
Grid value 11:    lambda = .0790106   no. of nonzero coef. =       2
Folds: 1...5....10   CVF =  1.21768
Grid value 12:    lambda = .0719916   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.214872
Grid value 13:    lambda =  .065596   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.211243
Grid value 14:    lambda = .0597687   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.207609
Grid value 15:    lambda =  .054459   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.204545
Grid value 16:    lambda =  .049621   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.201897
Grid value 17:    lambda = .0452128   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.199363
Grid value 18:    lambda = .0411962   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.197032
Grid value 19:    lambda = .0375365   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.194595
Grid value 20:    lambda = .0342018   no. of nonzero coef. =       6
Folds: 1...5....10   CVF =  1.19238
Grid value 21:    lambda = .0311634   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.190074
Grid value 22:    lambda =  .028395   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.188045
Grid value 23:    lambda = .0258724   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.186315
Grid value 24:    lambda =  .023574   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.184801
Grid value 25:    lambda = .0214797   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.183293
Grid value 26:    lambda = .0195715   no. of nonzero coef. =      10
Folds: 1...5....10   CVF = 1.181898
Grid value 27:    lambda = .0178329   no. of nonzero coef. =      11
Folds: 1...5....10   CVF =  1.18061
Grid value 28:    lambda = .0162486   no. of nonzero coef. =      14
Folds: 1...5....10   CVF = 1.179345
Grid value 29:    lambda = .0148052   no. of nonzero coef. =      15
Folds: 1...5....10   CVF = 1.178106
Grid value 30:    lambda = .0134899   no. of nonzero coef. =      17
Folds: 1...5....10   CVF = 1.176898
Grid value 31:    lambda = .0122915   no. of nonzero coef. =      20
Folds: 1...5....10   CVF = 1.175808
Grid value 32:    lambda = .0111996   no. of nonzero coef. =      24
Folds: 1...5....10   CVF = 1.174767
Grid value 33:    lambda = .0102046   no. of nonzero coef. =      28
Folds: 1...5....10   CVF = 1.173841
Grid value 34:    lambda = .0092981   no. of nonzero coef. =      34
Folds: 1...5....10   CVF = 1.172953
Grid value 35:    lambda = .0084721   no. of nonzero coef. =      39
Folds: 1...5....10   CVF = 1.172099
Grid value 36:    lambda = .0077194   no. of nonzero coef. =      44
Folds: 1...5....10   CVF = 1.171426
Grid value 37:    lambda = .0070336   no. of nonzero coef. =      48
Folds: 1...5....10   CVF = 1.170922
Grid value 38:    lambda = .0064088   no. of nonzero coef. =      57
Folds: 1...5....10   CVF = 1.170605
Grid value 39:    lambda = .0058395   no. of nonzero coef. =      71
Folds: 1...5....10   CVF = 1.170473
Grid value 40:    lambda = .0053207   no. of nonzero coef. =      80
Folds: 1...5....10   CVF =   1.1705
Grid value 41:    lambda =  .004848   no. of nonzero coef. =      92
Folds: 1...5....10   CVF = 1.170612
Grid value 42:    lambda = .0044173   no. of nonzero coef. =     103
Folds: 1...5....10   CVF = 1.170814
Grid value 43:    lambda = .0040249   no. of nonzero coef. =     115
Folds: 1...5....10   CVF = 1.171096
Grid value 44:    lambda = .0036673   no. of nonzero coef. =     123
Folds: 1...5....10   CVF = 1.171455
Grid value 45:    lambda = .0033416   no. of nonzero coef. =     133
Folds: 1...5....10   CVF = 1.171887
Grid value 46:    lambda = .0030447   no. of nonzero coef. =     142
Folds: 1...5....10   CVF = 1.172394
Grid value 47:    lambda = .0027742   no. of nonzero coef. =     150
Folds: 1...5....10   CVF = 1.172983
... cross-validation complete ... minimum found

alpha 3 of 3: alpha = 0.5
note: 18.kunta19 omitted because it is constant in C.V. subsamples.
note: 52.kunta19 omitted because it is constant in C.V. subsamples.
note: 97.kunta19 omitted because it is constant in C.V. subsamples.
note: 152.kunta19 omitted because it is constant in C.V. subsamples.
note: 181.kunta19 omitted because it is constant in C.V. subsamples.
note: 204.kunta19 omitted because it is constant in C.V. subsamples.
note: 213.kunta19 omitted because it is constant in C.V. subsamples.
note: 218.kunta19 omitted because it is constant in C.V. subsamples.
note: 235.kunta19 omitted because it is constant in C.V. subsamples.
note: 275.kunta19 omitted because it is constant in C.V. subsamples.
note: 284.kunta19 omitted because it is constant in C.V. subsamples.
note: 304.kunta19 omitted because it is constant in C.V. subsamples.
note: 320.kunta19 omitted because it is constant in C.V. subsamples.
note: 407.kunta19 omitted because it is constant in C.V. subsamples.
note: 580.kunta19 omitted because it is constant in C.V. subsamples.
note: 631.kunta19 omitted because it is constant in C.V. subsamples.
note: 681.kunta19 omitted because it is constant in C.V. subsamples.
note: 738.kunta19 omitted because it is constant in C.V. subsamples.
note: 742.kunta19 omitted because it is constant in C.V. subsamples.
note: 751.kunta19 omitted because it is constant in C.V. subsamples.
note: 846.kunta19 omitted because it is constant in C.V. subsamples.
note: 918.kunta19 omitted because it is constant in C.V. subsamples.
10-fold cross-validation with 109 lambdas ...
Grid value 1:     lambda = .1734282   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.23546
Grid value 2:     lambda = .1580213   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.230643
Grid value 3:     lambda = .1439831   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.226244
Grid value 4:     lambda = .1311921   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.222445
Grid value 5:     lambda = .1195373   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.219174
Grid value 6:     lambda = .1156188   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.218117
Grid value 7:     lambda = .1053475   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.215297
Grid value 8:     lambda = .0959887   no. of nonzero coef. =       3
Folds: 1...5....10   CVF =  1.21156
Grid value 9:     lambda = .0874614   no. of nonzero coef. =       3
Folds: 1...5....10   CVF =  1.20816
Grid value 10:    lambda = .0867141   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.207871
Grid value 11:    lambda = .0790106   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.204946
Grid value 12:    lambda = .0719916   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.202235
Grid value 13:    lambda =  .065596   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.199672
Grid value 14:    lambda = .0597687   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.197205
Grid value 15:    lambda =  .054459   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.194811
Grid value 16:    lambda =  .049621   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.192437
Grid value 17:    lambda = .0452128   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.190161
Grid value 18:    lambda = .0411962   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.188192
Grid value 19:    lambda = .0375365   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.186502
Grid value 20:    lambda = .0342018   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.184947
Grid value 21:    lambda = .0311634   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.183408
Grid value 22:    lambda =  .028395   no. of nonzero coef. =      10
Folds: 1...5....10   CVF = 1.182003
Grid value 23:    lambda = .0258724   no. of nonzero coef. =      12
Folds: 1...5....10   CVF = 1.180674
Grid value 24:    lambda =  .023574   no. of nonzero coef. =      14
Folds: 1...5....10   CVF = 1.179364
Grid value 25:    lambda = .0214797   no. of nonzero coef. =      15
Folds: 1...5....10   CVF = 1.178088
Grid value 26:    lambda = .0195715   no. of nonzero coef. =      18
Folds: 1...5....10   CVF = 1.176871
Grid value 27:    lambda = .0178329   no. of nonzero coef. =      21
Folds: 1...5....10   CVF = 1.175765
Grid value 28:    lambda = .0162486   no. of nonzero coef. =      27
Folds: 1...5....10   CVF = 1.174724
Grid value 29:    lambda = .0148052   no. of nonzero coef. =      30
Folds: 1...5....10   CVF = 1.173798
Grid value 30:    lambda = .0134899   no. of nonzero coef. =      35
Folds: 1...5....10   CVF = 1.172865
Grid value 31:    lambda = .0122915   no. of nonzero coef. =      40
Folds: 1...5....10   CVF =  1.17204
Grid value 32:    lambda = .0111996   no. of nonzero coef. =      45
Folds: 1...5....10   CVF = 1.171398
Grid value 33:    lambda = .0102046   no. of nonzero coef. =      53
Folds: 1...5....10   CVF = 1.170932
Grid value 34:    lambda = .0092981   no. of nonzero coef. =      61
Folds: 1...5....10   CVF =  1.17065
Grid value 35:    lambda = .0084721   no. of nonzero coef. =      76
Folds: 1...5....10   CVF = 1.170545
Grid value 36:    lambda = .0077194   no. of nonzero coef. =      87
Folds: 1...5....10   CVF = 1.170592
Grid value 37:    lambda = .0070336   no. of nonzero coef. =      96
Folds: 1...5....10   CVF = 1.170708
Grid value 38:    lambda = .0064088   no. of nonzero coef. =     105
Folds: 1...5....10   CVF = 1.170917
Grid value 39:    lambda = .0058395   no. of nonzero coef. =     118
Folds: 1...5....10   CVF = 1.171208
Grid value 40:    lambda = .0053207   no. of nonzero coef. =     127
Folds: 1...5....10   CVF = 1.171571
Grid value 41:    lambda =  .004848   no. of nonzero coef. =     136
Folds: 1...5....10   CVF = 1.172014
Grid value 42:    lambda = .0044173   no. of nonzero coef. =     147
Folds: 1...5....10   CVF = 1.172529
Grid value 43:    lambda = .0040249   no. of nonzero coef. =     155
Folds: 1...5....10   CVF = 1.173137
... cross-validation complete ... minimum found

Elastic net logit model                          No. of obs        =     19,879
                                                 No. of covariates =        261
Selection: Cross-validation                      No. of CV folds   =         10

-------------------------------------------------------------------------------
               |                               No. of      Out-of-
               |                              nonzero       sample      CV mean
alpha       ID |     Description      lambda    coef.   dev. ratio     deviance
---------------+---------------------------------------------------------------
1.000          |
             1 |    first lambda    .1734282        0      -0.0001     1.235664
            41 |   lambda before     .004848       54       0.0526     1.170568
          * 42 | selected lambda    .0044173       68       0.0527     1.170436
            43 |    lambda after    .0040249       78       0.0527     1.170462
            50 |     last lambda    .0020986      149       0.0507     1.172963
---------------+---------------------------------------------------------------
0.750          |
            51 |    first lambda    .1734282        0      -0.0001     1.235664
            97 |     last lambda    .0027742      150       0.0507     1.172983
---------------+---------------------------------------------------------------
0.500          |
            98 |    first lambda    .1734282        0       0.0001      1.23546
           140 |     last lambda    .0040249      155       0.0505     1.173137
-------------------------------------------------------------------------------
* alpha and lambda selected by cross-validation.

. predict pvote_enet_mother
(options pr penalized assumed; Pr(voted22) with penalized coefficients)

. 
. 
. 
. *Generate predicted voting for spillover sample (logit)
. logit voted22 molincome age female firstvote foreign i.moses1d i.mohighschool i.kunta19 if treatedf==0

note: 20.kunta19 != 0 predicts failure perfectly;
      20.kunta19 omitted and 4 obs not used.

note: 50.kunta19 != 0 predicts success perfectly;
      50.kunta19 omitted and 1 obs not used.

note: 78.kunta19 != 0 predicts failure perfectly;
      78.kunta19 omitted and 1 obs not used.

note: 98.kunta19 != 0 predicts failure perfectly;
      98.kunta19 omitted and 1 obs not used.

note: 99.kunta19 != 0 predicts failure perfectly;
      99.kunta19 omitted and 1 obs not used.

note: 171.kunta19 != 0 predicts failure perfectly;
      171.kunta19 omitted and 2 obs not used.

note: 181.kunta19 != 0 predicts failure perfectly;
      181.kunta19 omitted and 2 obs not used.

note: 204.kunta19 != 0 predicts success perfectly;
      204.kunta19 omitted and 1 obs not used.

note: 232.kunta19 != 0 predicts failure perfectly;
      232.kunta19 omitted and 1 obs not used.

note: 235.kunta19 != 0 predicts failure perfectly;
      235.kunta19 omitted and 1 obs not used.

note: 263.kunta19 != 0 predicts failure perfectly;
      263.kunta19 omitted and 1 obs not used.

note: 275.kunta19 != 0 predicts failure perfectly;
      275.kunta19 omitted and 1 obs not used.

note: 284.kunta19 != 0 predicts failure perfectly;
      284.kunta19 omitted and 1 obs not used.

note: 291.kunta19 != 0 predicts success perfectly;
      291.kunta19 omitted and 1 obs not used.

note: 301.kunta19 != 0 predicts success perfectly;
      301.kunta19 omitted and 1 obs not used.

note: 312.kunta19 != 0 predicts failure perfectly;
      312.kunta19 omitted and 2 obs not used.

note: 320.kunta19 != 0 predicts failure perfectly;
      320.kunta19 omitted and 1 obs not used.

note: 399.kunta19 != 0 predicts success perfectly;
      399.kunta19 omitted and 1 obs not used.

note: 400.kunta19 != 0 predicts failure perfectly;
      400.kunta19 omitted and 4 obs not used.

note: 433.kunta19 != 0 predicts success perfectly;
      433.kunta19 omitted and 2 obs not used.

note: 478.kunta19 != 0 predicts failure perfectly;
      478.kunta19 omitted and 1 obs not used.

note: 484.kunta19 != 0 predicts failure perfectly;
      484.kunta19 omitted and 1 obs not used.

note: 560.kunta19 != 0 predicts failure perfectly;
      560.kunta19 omitted and 1 obs not used.

note: 576.kunta19 != 0 predicts failure perfectly;
      576.kunta19 omitted and 1 obs not used.

note: 599.kunta19 != 0 predicts failure perfectly;
      599.kunta19 omitted and 1 obs not used.

note: 611.kunta19 != 0 predicts failure perfectly;
      611.kunta19 omitted and 1 obs not used.

note: 631.kunta19 != 0 predicts failure perfectly;
      631.kunta19 omitted and 1 obs not used.

note: 689.kunta19 != 0 predicts failure perfectly;
      689.kunta19 omitted and 1 obs not used.

note: 704.kunta19 != 0 predicts failure perfectly;
      704.kunta19 omitted and 1 obs not used.

note: 710.kunta19 != 0 predicts failure perfectly;
      710.kunta19 omitted and 5 obs not used.

note: 732.kunta19 != 0 predicts failure perfectly;
      732.kunta19 omitted and 2 obs not used.

note: 748.kunta19 != 0 predicts failure perfectly;
      748.kunta19 omitted and 1 obs not used.

note: 751.kunta19 != 0 predicts failure perfectly;
      751.kunta19 omitted and 1 obs not used.

note: 758.kunta19 != 0 predicts failure perfectly;
      758.kunta19 omitted and 2 obs not used.

note: 777.kunta19 != 0 predicts success perfectly;
      777.kunta19 omitted and 1 obs not used.

note: 791.kunta19 != 0 predicts failure perfectly;
      791.kunta19 omitted and 1 obs not used.

note: 833.kunta19 != 0 predicts failure perfectly;
      833.kunta19 omitted and 1 obs not used.

note: 846.kunta19 != 0 predicts failure perfectly;
      846.kunta19 omitted and 1 obs not used.

note: 854.kunta19 != 0 predicts success perfectly;
      854.kunta19 omitted and 1 obs not used.

note: 859.kunta19 != 0 predicts success perfectly;
      859.kunta19 omitted and 2 obs not used.

note: 892.kunta19 != 0 predicts success perfectly;
      892.kunta19 omitted and 1 obs not used.

note: 980.kunta19 != 0 predicts failure perfectly;
      980.kunta19 omitted and 6 obs not used.

note: firstvote omitted because of collinearity.
Iteration 0:   log likelihood = -10118.354  
Iteration 1:   log likelihood = -8913.5047  
Iteration 2:   log likelihood = -8910.0377  
Iteration 3:   log likelihood = -8910.0201  
Iteration 4:   log likelihood = -8910.0201  

Logistic regression                                    Number of obs =  14,598
                                                       LR chi2(193)  = 2416.67
                                                       Prob > chi2   =  0.0000
Log likelihood = -8910.0201                            Pseudo R2     =  0.1194

--------------------------------------------------------------------------------
       voted22 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
     molincome |   .1413973   .0316181     4.47   0.000      .079427    .2033677
           age |   .0400465   .0014103    28.40   0.000     .0372824    .0428105
        female |   .0804656   .0388012     2.07   0.038     .0044166    .1565145
     firstvote |          0  (omitted)
       foreign |  -1.226083   .1156527   -10.60   0.000    -1.452758   -.9994077
               |
       moses1d |
            2  |  -.4558254   .1393587    -3.27   0.001    -.7289635   -.1826874
            3  |  -.1418779   .1367351    -1.04   0.299    -.4098738     .126118
            4  |  -.5778178    .130366    -4.43   0.000    -.8333305    -.322305
            5  |  -.9307551    .130813    -7.12   0.000    -1.187144   -.6743663
            6  |  -.3213773   .1738696    -1.85   0.065    -.6621555    .0194008
            7  |  -.9705218   .1474683    -6.58   0.000    -1.259554   -.6814892
            8  |  -1.030648   .1485433    -6.94   0.000    -1.321788   -.7395085
            9  |  -.6496727   .1822324    -3.57   0.000    -1.006842   -.2925037
               |
1.mohighschool |   .6374865   .0414428    15.38   0.000     .5562601     .718713
               |
       kunta19 |
            9  |   .6802607   .4601901     1.48   0.139    -.2216954    1.582217
           10  |  -.6388067    .281027    -2.27   0.023     -1.18961   -.0880039
           19  |  -1.285026   .4874542    -2.64   0.008    -2.240418   -.3296331
           20  |          0  (empty)
           49  |   .6605008   .3958032     1.67   0.095    -.1152593    1.436261
           50  |          0  (empty)
           51  |  -.6090112   .3230431    -1.89   0.059    -1.242164    .0241417
           61  |   -.302954   .8838838    -0.34   0.732    -2.035335    1.429426
           69  |   .3759502   .3261648     1.15   0.249     -.263321    1.015222
           71  |   .5261567   .2923194     1.80   0.072    -.0467788    1.099092
           72  |   .0609876   1.066061     0.06   0.954    -2.028453    2.150429
           74  |   .2683257   .6328599     0.42   0.672     -.972057    1.508708
           77  |  -.0178288   .3908367    -0.05   0.964    -.7838546     .748197
           78  |          0  (empty)
           79  |  -.4092727   .3984529    -1.03   0.304    -1.190226    .3716807
           86  |  -.2146389   .3724404    -0.58   0.564    -.9446086    .5153309
           91  |   .0869407    .292689     0.30   0.766    -.4867191    .6606006
           92  |  -.2942355   .2155331    -1.37   0.172    -.7166725    .1282016
           98  |          0  (empty)
           99  |          0  (empty)
          106  |  -.6671196   .8676226    -0.77   0.442    -2.367629    1.033389
          108  |  -.6463609   .3211564    -2.01   0.044    -1.275816    -.016906
          109  |  -.5346151   .5185702    -1.03   0.303    -1.550994    .4817639
          111  |   -.031968   1.601576    -0.02   0.984       -3.171    3.107064
          139  |   .9800873   1.095455     0.89   0.371    -1.166965    3.127139
          140  |  -1.794358   1.069733    -1.68   0.093    -3.890997    .3022801
          146  |  -.4750195    .482921    -0.98   0.325    -1.421527    .4714883
          148  |   -.964074   1.244225    -0.77   0.438    -3.402711    1.474563
          151  |   .0473821   .5315938     0.09   0.929    -.9945227    1.089287
          153  |   .1868643   1.244591     0.15   0.881    -2.252489    2.626217
          165  |   -.064448   .2914161    -0.22   0.825    -.6356131    .5067172
          167  |  -.0345346   .2276452    -0.15   0.879    -.4807109    .4116418
          169  |   .3375078   .4144932     0.81   0.415    -.4748839    1.149899
          171  |          0  (empty)
          177  |  -.1137821   .5801284    -0.20   0.845    -1.250813    1.023249
          179  |  -.0531737   .2191409    -0.24   0.808    -.4826821    .3763346
          181  |          0  (empty)
          182  |   -.110249   .2644594    -0.42   0.677    -.6285799    .4080818
          186  |   .0112465   .6779572     0.02   0.987    -1.317525    1.340018
          202  |  -.2062856   .2631413    -0.78   0.433    -.7220331     .309462
          204  |          0  (empty)
          205  |   -.493251   .4559925    -1.08   0.279     -1.38698    .4004778
          208  |   1.195362   1.251574     0.96   0.340    -1.257678    3.648402
          211  |  -.0216153   .6027654    -0.04   0.971    -1.203014    1.159783
          216  |  -.6980148   .6621979    -1.05   0.292    -1.995899    .5998692
          217  |   .1650037   .3607786     0.46   0.647    -.5421093    .8721167
          224  |  -.2295099   .3689127    -0.62   0.534    -.9525656    .4935457
          226  |  -.3642025   .4371637    -0.83   0.405    -1.221028    .4926227
          230  |  -.0999886   .5470112    -0.18   0.855    -1.172111    .9721336
          231  |  -1.127588   .7734011    -1.46   0.145    -2.643426    .3882505
          232  |          0  (empty)
          233  |  -.3468085   1.243602    -0.28   0.780    -2.784224    2.090607
          235  |          0  (empty)
          236  |   .8303188   .4005874     2.07   0.038     .0451821    1.615456
          239  |   .3296438    .564757     0.58   0.559    -.7772595    1.436547
          240  |  -.1954889   .2806283    -0.70   0.486    -.7455101    .3545324
          241  |  -.5319843    1.02451    -0.52   0.604    -2.539986    1.476018
          244  |   .0664903   .2760571     0.24   0.810    -.4745717    .6075522
          245  |  -.0606374   .2551641    -0.24   0.812    -.5607497     .439475
          249  |   1.173878   1.449042     0.81   0.418    -1.666193    4.013948
          257  |  -.2240901   .2588115    -0.87   0.387    -.7313514    .2831711
          260  |  -1.302844   1.278159    -1.02   0.308    -3.807989    1.202302
          261  |  -.4294371   1.293856    -0.33   0.740    -2.965348    2.106474
          263  |          0  (empty)
          265  |  -.4220327    .769261    -0.55   0.583    -1.929757    1.085691
          272  |   .1030244   .5798442     0.18   0.859    -1.033449    1.239498
          273  |   .6393597   1.459488     0.44   0.661    -2.221184    3.499903
          275  |          0  (empty)
          276  |  -.3014348   .2815197    -1.07   0.284    -.8532034    .2503337
          284  |          0  (empty)
          285  |  -.4324082   .8835367    -0.49   0.625    -2.164108    1.299292
          286  |   .9039689   .9834713     0.92   0.358    -1.023599    2.831537
          287  |  -.2438629   .3418483    -0.71   0.476    -.9138732    .4261474
          291  |          0  (empty)
          297  |    .019781   .4102835     0.05   0.962    -.7843599    .8239219
          300  |  -.5279244   .4286357    -1.23   0.218    -1.368035    .3121862
          301  |          0  (empty)
          305  |  -.7148945   .2862284    -2.50   0.013    -1.275892   -.1538971
          309  |  -.4477968   .4000814    -1.12   0.263    -1.231942    .3363483
          312  |          0  (empty)
          317  |   1.118472   1.432224     0.78   0.435    -1.688635    3.925579
          320  |          0  (empty)
          322  |   .1594752   .3958958     0.40   0.687    -.6164664    .9354168
          398  |  -.6566886   .5703208    -1.15   0.250    -1.774497    .4611196
          399  |          0  (empty)
          400  |          0  (empty)
          402  |   .5154358   .3685624     1.40   0.162    -.2069332    1.237805
          403  |   -.594729   .5222825    -1.14   0.255    -1.618384     .428926
          405  |   .1028356   .6260946     0.16   0.870    -1.124287    1.329958
          407  |   1.011607   1.474929     0.69   0.493    -1.879202    3.902415
          408  |  -.4247787   1.219095    -0.35   0.728    -2.814162    1.964605
          410  |  -.2327355   .2837818    -0.82   0.412    -.7889376    .3234665
          418  |  -.0791063    .771527    -0.10   0.918    -1.591271    1.433059
          422  |  -.1098036   .3393424    -0.32   0.746    -.7749024    .5552953
          423  |   1.853897   1.179587     1.57   0.116    -.4580505    4.165844
          425  |   .2009839   .2951079     0.68   0.496    -.3774169    .7793847
          426  |   .3812402    .341025     1.12   0.264    -.2871565    1.049637
          433  |          0  (empty)
          434  |   1.203644   1.216734     0.99   0.323     -1.18111    3.588398
          436  |   .2169986   .5443423     0.40   0.690    -.8498928     1.28389
          441  |  -.2620774   .5089422    -0.51   0.607    -1.259586     .735431
          444  |  -.5351722   .2372303    -2.26   0.024    -1.000135   -.0702094
          445  |  -.0124535   .3094538    -0.04   0.968    -.6189718    .5940648
          475  |   .4385066    .367413     1.19   0.233    -.2816097    1.158623
          478  |          0  (empty)
          481  |  -.0079112   .3457968    -0.02   0.982    -.6856604    .6698379
          484  |          0  (empty)
          491  |  -.8063436   .7225634    -1.12   0.264    -2.222542    .6098546
          494  |  -.0571579   .3424966    -0.17   0.867     -.728439    .6141232
          498  |   .3111231   1.266451     0.25   0.806    -2.171076    2.793322
          499  |   .5364947   .9541285     0.56   0.574    -1.333563    2.406552
          500  |    .221755   .3394023     0.65   0.514    -.4434613    .8869713
          503  |  -.9843891   .3717892    -2.65   0.008    -1.713083   -.2556957
          505  |  -1.045668   .8320287    -1.26   0.209    -2.676415    .5850779
          507  |  -.4310729   .3750767    -1.15   0.250     -1.16621    .3040639
          508  |  -.4143131   1.251383    -0.33   0.741    -2.866978    2.038352
          529  |   .1546815   .8993157     0.17   0.863    -1.607945    1.917308
          531  |   .2112386    .384445     0.55   0.583    -.5422598     .964737
          535  |    .605019   .2808428     2.15   0.031     .0545772    1.155461
          536  |  -.1850484   .7429799    -0.25   0.803    -1.641262    1.271165
          541  |  -.0223089   .8486171    -0.03   0.979    -1.685568     1.64095
          543  |  -.2956087   .2382396    -1.24   0.215    -.7625497    .1713324
          545  |  -.5255545   .9967007    -0.53   0.598    -2.479052    1.427943
          560  |          0  (empty)
          562  |  -.3544047   .8801269    -0.40   0.687    -2.079422    1.370612
          563  |   .4658492   .9467445     0.49   0.623    -1.389736    2.321434
          564  |   .0121019   .2145955     0.06   0.955    -.4084975    .4327013
          576  |          0  (empty)
          577  |  -.2861254   .3154937    -0.91   0.364    -.9044817    .3322309
          578  |   .3616848     .50097     0.72   0.470    -.6201984    1.343568
          581  |   .0612535   .4064217     0.15   0.880    -.7353184    .8578255
          583  |  -.0586853   .8186534    -0.07   0.943    -1.663216    1.545846
          584  |   .0423292   .4379684     0.10   0.923     -.816073    .9007315
          588  |  -.8944926   .8890948    -1.01   0.314    -2.637086    .8481011
          592  |   1.057844   .5868865     1.80   0.071    -.0924322    2.208121
          593  |  -.4779062   .2965153    -1.61   0.107    -1.059066    .1032531
          598  |  -.2559104   .8817859    -0.29   0.772    -1.984179    1.472358
          599  |          0  (empty)
          601  |  -.2641876   1.076271    -0.25   0.806    -2.373639    1.845264
          604  |   .9817834   1.292173     0.76   0.447    -1.550829    3.514395
          607  |   .5763547   .5266599     1.09   0.274    -.4558797    1.608589
          608  |  -.5743772   .5257021    -1.09   0.275    -1.604734    .4559799
          609  |  -.2383942   .2280907    -1.05   0.296    -.6854438    .2086553
          611  |          0  (empty)
          614  |   -.412085   .4385134    -0.94   0.347    -1.271556    .4473855
          615  |  -.7955639   .7582629    -1.05   0.294    -2.281732    .6906041
          620  |   .1741099   .7490578     0.23   0.816    -1.294016    1.642236
          623  |  -.5571635   .5288985    -1.05   0.292    -1.593786    .4794586
          624  |  -.1535685    .375357    -0.41   0.682    -.8892547    .5821178
          625  |   1.008647   1.289565     0.78   0.434    -1.518854    3.536147
          626  |  -.3423511   .4612926    -0.74   0.458    -1.246468    .5617658
          631  |          0  (empty)
          635  |    .052062   .3738584     0.14   0.889     -.680687    .7848109
          638  |   -.524148   .2518083    -2.08   0.037    -1.017683   -.0306129
          678  |   .4829958   .6146927     0.79   0.432    -.7217798    1.687771
          680  |  -.2385217   .2801212    -0.85   0.394    -.7875492    .3105058
          683  |   .5341677   .3941549     1.36   0.175    -.2383617    1.306697
          684  |   .0604613   .7298157     0.08   0.934    -1.369951    1.490874
          687  |  -.2908069    1.02285    -0.28   0.776    -2.295556    1.713942
          689  |          0  (empty)
          691  |   -.345847   .4413494    -0.78   0.433    -1.210876    .5191819
          694  |  -.5881169   1.128937    -0.52   0.602    -2.800793    1.624559
          697  |  -1.025072   .7706044    -1.33   0.183    -2.535429    .4852849
          698  |   -.182823   .2326854    -0.79   0.432     -.638878    .2732319
          704  |          0  (empty)
          710  |          0  (empty)
          729  |   .2379093   .3190429     0.75   0.456    -.3874034     .863222
          732  |          0  (empty)
          734  |  -.1220361   .2381495    -0.51   0.608    -.5888005    .3447283
          740  |   2.030589   1.135009     1.79   0.074    -.1939876    4.255166
          743  |  -.9728782   .6982545    -1.39   0.164    -2.341432    .3956755
          746  |   .6601514   .3085381     2.14   0.032     .0554279    1.264875
          747  |    .438699   .5611361     0.78   0.434    -.6611076    1.538506
          748  |          0  (empty)
          749  |   .8491065   1.265564     0.67   0.502    -1.631354    3.329567
          751  |          0  (empty)
          753  |  -.3120694   .2912883    -1.07   0.284     -.882984    .2588452
          755  |   .0142259   1.454641     0.01   0.992    -2.836819    2.865271
          758  |          0  (empty)
          759  |   .4131629   .5115281     0.81   0.419    -.5894138     1.41574
          761  |   .0231115   .3499251     0.07   0.947    -.6627291    .7089521
          762  |    .159239   .5064338     0.31   0.753    -.8333529    1.151831
          765  |   -.180034   .3166338    -0.57   0.570    -.8006247    .4405568
          768  |  -.5541155   .6883984    -0.80   0.421    -1.903352    .7951206
          777  |          0  (empty)
          778  |   .6934711   1.456895     0.48   0.634    -2.161991    3.548934
          781  |  -1.137069   .5827171    -1.95   0.051    -2.279173    .0050358
          785  |    -.98381   .5228581    -1.88   0.060    -2.008593     .040973
          790  |   .1087045   1.034264     0.11   0.916    -1.918416    2.135825
          791  |          0  (empty)
          832  |   .6408284   1.436717     0.45   0.656    -2.175086    3.456743
          833  |          0  (empty)
          834  |   .6310294   1.332365     0.47   0.636    -1.980358    3.242416
          837  |   .0786074   .2150293     0.37   0.715    -.3428423     .500057
          845  |  -.1778772   .5379723    -0.33   0.741    -1.232284    .8765292
          846  |          0  (empty)
          849  |   1.036008   .4914531     2.11   0.035     .0727773    1.999238
          850  |  -1.258231   .6021114    -2.09   0.037    -2.438348   -.0781148
          851  |  -.1956785   .7963612    -0.25   0.806    -1.756518    1.365161
          853  |   .3122341   .3082334     1.01   0.311    -.2918923    .9163606
          854  |          0  (empty)
          858  |   .8481268   .7001197     1.21   0.226    -.5240826    2.220336
          859  |          0  (empty)
          886  |   -.914835   1.116482    -0.82   0.413    -3.103099    1.273429
          889  |  -1.286151   .5279618    -2.44   0.015    -2.320937   -.2513646
          892  |          0  (empty)
          893  |   .7624366   1.430748     0.53   0.594    -2.041778    3.566651
          895  |  -.1969102   .2913696    -0.68   0.499    -.7679842    .3741638
          905  |  -.0451953    .231607    -0.20   0.845    -.4991367    .4087461
          908  |  -.5318128   .2835027    -1.88   0.061    -1.087468    .0238423
          915  |   .4718066    1.47388     0.32   0.749    -2.416945    3.360558
          924  |    .584051   1.475551     0.40   0.692    -2.307976    3.476078
          925  |  -.3538625   .4619357    -0.77   0.444     -1.25924    .5515148
          927  |  -.4440146    .252221    -1.76   0.078    -.9383587    .0503296
          931  |  -.4944006   .4020877    -1.23   0.219    -1.282478    .2936768
          935  |   .2749259    .536263     0.51   0.608    -.7761304    1.325982
          946  |    .604373   .3263632     1.85   0.064    -.0352871    1.244033
          977  |   .0370767   .2801382     0.13   0.895    -.5119842    .5861376
          980  |          0  (empty)
          981  |   -.362957   .4989499    -0.73   0.467    -1.340881     .614967
          989  |  -.4176278   .3894015    -1.07   0.284    -1.180841     .345585
          992  |  -1.158472   1.245376    -0.93   0.352    -3.599365     1.28242
               |
         _cons |  -2.659545   .3882691    -6.85   0.000    -3.420538   -1.898551
--------------------------------------------------------------------------------

. predict pvoteold
(option pr assumed; Pr(voted22))
(355,097 missing values generated)

. 
. *Generate predicted voting for spillover sample (enet logit)
. elasticnet logit voted22 molincome age female firstvote foreign i.moses1d i.mohighschool i.kunta19 if treatedf==
> 0

alpha 1 of 3: alpha = 1
note: firstvote omitted because it is constant.
note: 46.kunta19 omitted because it is constant.
note: 75.kunta19 omitted because it is constant.
note: 81.kunta19 omitted because it is constant.
note: 102.kunta19 omitted because it is constant.
note: 143.kunta19 omitted because it is constant.
note: 145.kunta19 omitted because it is constant.
note: 152.kunta19 omitted because it is constant.
note: 172.kunta19 omitted because it is constant.
note: 176.kunta19 omitted because it is constant.
note: 178.kunta19 omitted because it is constant.
note: 213.kunta19 omitted because it is constant.
note: 214.kunta19 omitted because it is constant.
note: 218.kunta19 omitted because it is constant.
note: 271.kunta19 omitted because it is constant.
note: 280.kunta19 omitted because it is constant.
note: 288.kunta19 omitted because it is constant.
note: 420.kunta19 omitted because it is constant.
note: 430.kunta19 omitted because it is constant.
note: 483.kunta19 omitted because it is constant.
note: 489.kunta19 omitted because it is constant.
note: 619.kunta19 omitted because it is constant.
note: 636.kunta19 omitted because it is constant.
note: 686.kunta19 omitted because it is constant.
note: 702.kunta19 omitted because it is constant.
note: 831.kunta19 omitted because it is constant.
note: 848.kunta19 omitted because it is constant.
note: 922.kunta19 omitted because it is constant.
note: 934.kunta19 omitted because it is constant.
note: 976.kunta19 omitted because it is constant.
note: 50.kunta19 omitted because it is constant in C.V. subsamples.
note: 78.kunta19 omitted because it is constant in C.V. subsamples.
note: 98.kunta19 omitted because it is constant in C.V. subsamples.
note: 99.kunta19 omitted because it is constant in C.V. subsamples.
note: 204.kunta19 omitted because it is constant in C.V. subsamples.
note: 232.kunta19 omitted because it is constant in C.V. subsamples.
note: 235.kunta19 omitted because it is constant in C.V. subsamples.
note: 263.kunta19 omitted because it is constant in C.V. subsamples.
note: 275.kunta19 omitted because it is constant in C.V. subsamples.
note: 284.kunta19 omitted because it is constant in C.V. subsamples.
note: 291.kunta19 omitted because it is constant in C.V. subsamples.
note: 301.kunta19 omitted because it is constant in C.V. subsamples.
note: 317.kunta19 omitted because it is constant in C.V. subsamples.
note: 320.kunta19 omitted because it is constant in C.V. subsamples.
note: 399.kunta19 omitted because it is constant in C.V. subsamples.
note: 478.kunta19 omitted because it is constant in C.V. subsamples.
note: 484.kunta19 omitted because it is constant in C.V. subsamples.
note: 560.kunta19 omitted because it is constant in C.V. subsamples.
note: 576.kunta19 omitted because it is constant in C.V. subsamples.
note: 599.kunta19 omitted because it is constant in C.V. subsamples.
note: 611.kunta19 omitted because it is constant in C.V. subsamples.
note: 631.kunta19 omitted because it is constant in C.V. subsamples.
note: 689.kunta19 omitted because it is constant in C.V. subsamples.
note: 704.kunta19 omitted because it is constant in C.V. subsamples.
note: 748.kunta19 omitted because it is constant in C.V. subsamples.
note: 751.kunta19 omitted because it is constant in C.V. subsamples.
note: 755.kunta19 omitted because it is constant in C.V. subsamples.
note: 777.kunta19 omitted because it is constant in C.V. subsamples.
note: 791.kunta19 omitted because it is constant in C.V. subsamples.
note: 832.kunta19 omitted because it is constant in C.V. subsamples.
note: 833.kunta19 omitted because it is constant in C.V. subsamples.
note: 846.kunta19 omitted because it is constant in C.V. subsamples.
note: 854.kunta19 omitted because it is constant in C.V. subsamples.
note: 859.kunta19 omitted because it is constant in C.V. subsamples.
note: 892.kunta19 omitted because it is constant in C.V. subsamples.
10-fold cross-validation with 109 lambdas ...
Grid value 1:     lambda = .2500247   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 2:     lambda = .2278132   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 3:     lambda = .2075749   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 4:     lambda = .1891345   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 5:     lambda = .1723324   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 6:     lambda = .1666832   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 7:     lambda = .1518755   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 8:     lambda = .1383833   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 9:     lambda = .1260897   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.386153
Grid value 10:    lambda = .1250124   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38578
Grid value 11:    lambda = .1139066   no. of nonzero coef. =       1
Folds: 1...5....10   CVF = 1.375914
Grid value 12:    lambda = .1037875   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.366515
Grid value 13:    lambda = .0945673   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.351919
Grid value 14:    lambda = .0861662   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.339416
Grid value 15:    lambda = .0785114   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.328906
Grid value 16:    lambda = .0715367   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.318787
Grid value 17:    lambda = .0651816   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.310082
Grid value 18:    lambda =  .059391   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.302644
Grid value 19:    lambda = .0541149   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.295097
Grid value 20:    lambda = .0493075   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.288392
Grid value 21:    lambda = .0449271   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.282391
Grid value 22:    lambda = .0409359   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.277148
Grid value 23:    lambda = .0372993   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.272613
Grid value 24:    lambda = .0339857   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.268654
Grid value 25:    lambda = .0309665   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.265286
Grid value 26:    lambda = .0282155   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.262441
Grid value 27:    lambda = .0257089   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.260039
Grid value 28:    lambda =  .023425   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.257961
Grid value 29:    lambda =  .021344   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.255934
Grid value 30:    lambda = .0194479   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.254013
Grid value 31:    lambda = .0177202   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.252364
Grid value 32:    lambda =  .016146   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.250964
Grid value 33:    lambda = .0147116   no. of nonzero coef. =      10
Folds: 1...5....10   CVF = 1.249684
Grid value 34:    lambda = .0134047   no. of nonzero coef. =      10
Folds: 1...5....10   CVF = 1.248503
Grid value 35:    lambda = .0122138   no. of nonzero coef. =      15
Folds: 1...5....10   CVF =  1.24737
Grid value 36:    lambda = .0111288   no. of nonzero coef. =      17
Folds: 1...5....10   CVF =  1.24625
Grid value 37:    lambda = .0101401   no. of nonzero coef. =      21
Folds: 1...5....10   CVF =  1.24516
Grid value 38:    lambda = .0092393   no. of nonzero coef. =      24
Folds: 1...5....10   CVF = 1.244085
Grid value 39:    lambda = .0084185   no. of nonzero coef. =      26
Folds: 1...5....10   CVF = 1.243023
Grid value 40:    lambda = .0076706   no. of nonzero coef. =      37
Folds: 1...5....10   CVF = 1.242044
Grid value 41:    lambda = .0069892   no. of nonzero coef. =      45
Folds: 1...5....10   CVF = 1.241163
Grid value 42:    lambda = .0063683   no. of nonzero coef. =      47
Folds: 1...5....10   CVF = 1.240371
Grid value 43:    lambda = .0058026   no. of nonzero coef. =      50
Folds: 1...5....10   CVF = 1.239801
Grid value 44:    lambda = .0052871   no. of nonzero coef. =      55
Folds: 1...5....10   CVF = 1.239414
Grid value 45:    lambda = .0048174   no. of nonzero coef. =      65
Folds: 1...5....10   CVF = 1.239208
Grid value 46:    lambda = .0043894   no. of nonzero coef. =      69
Folds: 1...5....10   CVF = 1.239161
Grid value 47:    lambda = .0039995   no. of nonzero coef. =      76
Folds: 1...5....10   CVF = 1.239297
Grid value 48:    lambda = .0036442   no. of nonzero coef. =      89
Folds: 1...5....10   CVF = 1.239584
Grid value 49:    lambda = .0033204   no. of nonzero coef. =      96
Folds: 1...5....10   CVF = 1.240003
Grid value 50:    lambda = .0030255   no. of nonzero coef. =     109
Folds: 1...5....10   CVF = 1.240439
Grid value 51:    lambda = .0027567   no. of nonzero coef. =     118
Folds: 1...5....10   CVF =  1.24093
Grid value 52:    lambda = .0025118   no. of nonzero coef. =     123
Folds: 1...5....10   CVF = 1.241488
... cross-validation complete ... minimum found

alpha 2 of 3: alpha = 0.75
note: firstvote omitted because it is constant.
note: 46.kunta19 omitted because it is constant.
note: 75.kunta19 omitted because it is constant.
note: 81.kunta19 omitted because it is constant.
note: 102.kunta19 omitted because it is constant.
note: 143.kunta19 omitted because it is constant.
note: 145.kunta19 omitted because it is constant.
note: 152.kunta19 omitted because it is constant.
note: 172.kunta19 omitted because it is constant.
note: 176.kunta19 omitted because it is constant.
note: 178.kunta19 omitted because it is constant.
note: 213.kunta19 omitted because it is constant.
note: 214.kunta19 omitted because it is constant.
note: 218.kunta19 omitted because it is constant.
note: 271.kunta19 omitted because it is constant.
note: 280.kunta19 omitted because it is constant.
note: 288.kunta19 omitted because it is constant.
note: 420.kunta19 omitted because it is constant.
note: 430.kunta19 omitted because it is constant.
note: 483.kunta19 omitted because it is constant.
note: 489.kunta19 omitted because it is constant.
note: 619.kunta19 omitted because it is constant.
note: 636.kunta19 omitted because it is constant.
note: 686.kunta19 omitted because it is constant.
note: 702.kunta19 omitted because it is constant.
note: 831.kunta19 omitted because it is constant.
note: 848.kunta19 omitted because it is constant.
note: 922.kunta19 omitted because it is constant.
note: 934.kunta19 omitted because it is constant.
note: 976.kunta19 omitted because it is constant.
note: 50.kunta19 omitted because it is constant in C.V. subsamples.
note: 78.kunta19 omitted because it is constant in C.V. subsamples.
note: 98.kunta19 omitted because it is constant in C.V. subsamples.
note: 99.kunta19 omitted because it is constant in C.V. subsamples.
note: 204.kunta19 omitted because it is constant in C.V. subsamples.
note: 232.kunta19 omitted because it is constant in C.V. subsamples.
note: 235.kunta19 omitted because it is constant in C.V. subsamples.
note: 263.kunta19 omitted because it is constant in C.V. subsamples.
note: 275.kunta19 omitted because it is constant in C.V. subsamples.
note: 284.kunta19 omitted because it is constant in C.V. subsamples.
note: 291.kunta19 omitted because it is constant in C.V. subsamples.
note: 301.kunta19 omitted because it is constant in C.V. subsamples.
note: 317.kunta19 omitted because it is constant in C.V. subsamples.
note: 320.kunta19 omitted because it is constant in C.V. subsamples.
note: 399.kunta19 omitted because it is constant in C.V. subsamples.
note: 478.kunta19 omitted because it is constant in C.V. subsamples.
note: 484.kunta19 omitted because it is constant in C.V. subsamples.
note: 560.kunta19 omitted because it is constant in C.V. subsamples.
note: 576.kunta19 omitted because it is constant in C.V. subsamples.
note: 599.kunta19 omitted because it is constant in C.V. subsamples.
note: 611.kunta19 omitted because it is constant in C.V. subsamples.
note: 631.kunta19 omitted because it is constant in C.V. subsamples.
note: 689.kunta19 omitted because it is constant in C.V. subsamples.
note: 704.kunta19 omitted because it is constant in C.V. subsamples.
note: 748.kunta19 omitted because it is constant in C.V. subsamples.
note: 751.kunta19 omitted because it is constant in C.V. subsamples.
note: 755.kunta19 omitted because it is constant in C.V. subsamples.
note: 777.kunta19 omitted because it is constant in C.V. subsamples.
note: 791.kunta19 omitted because it is constant in C.V. subsamples.
note: 832.kunta19 omitted because it is constant in C.V. subsamples.
note: 833.kunta19 omitted because it is constant in C.V. subsamples.
note: 846.kunta19 omitted because it is constant in C.V. subsamples.
note: 854.kunta19 omitted because it is constant in C.V. subsamples.
note: 859.kunta19 omitted because it is constant in C.V. subsamples.
note: 892.kunta19 omitted because it is constant in C.V. subsamples.
10-fold cross-validation with 109 lambdas ...
Grid value 1:     lambda = .2500247   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 2:     lambda = .2278132   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 3:     lambda = .2075749   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 4:     lambda = .1891345   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 5:     lambda = .1723324   no. of nonzero coef. =       0
Folds: 1...5....10   CVF =  1.38637
Grid value 6:     lambda = .1666832   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.385864
Grid value 7:     lambda = .1518755   no. of nonzero coef. =       1
Folds: 1...5....10   CVF = 1.377236
Grid value 8:     lambda = .1383833   no. of nonzero coef. =       3
Folds: 1...5....10   CVF =  1.36873
Grid value 9:     lambda = .1260897   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.355055
Grid value 10:    lambda = .1250124   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.353868
Grid value 11:    lambda = .1139066   no. of nonzero coef. =       3
Folds: 1...5....10   CVF =  1.34204
Grid value 12:    lambda = .1037875   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.331646
Grid value 13:    lambda = .0945673   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.321571
Grid value 14:    lambda = .0861662   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.312941
Grid value 15:    lambda = .0785114   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.305345
Grid value 16:    lambda = .0715367   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.297623
Grid value 17:    lambda = .0651816   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.290773
Grid value 18:    lambda =  .059391   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.284558
Grid value 19:    lambda = .0541149   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.279091
Grid value 20:    lambda = .0493075   no. of nonzero coef. =       7
Folds: 1...5....10   CVF =  1.27429
Grid value 21:    lambda = .0449271   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.270173
Grid value 22:    lambda = .0409359   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.266668
Grid value 23:    lambda = .0372993   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.263685
Grid value 24:    lambda = .0339857   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.261149
Grid value 25:    lambda = .0309665   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.258922
Grid value 26:    lambda = .0282155   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.256738
Grid value 27:    lambda = .0257089   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.254711
Grid value 28:    lambda =  .023425   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.252983
Grid value 29:    lambda =  .021344   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.251521
Grid value 30:    lambda = .0194479   no. of nonzero coef. =      11
Folds: 1...5....10   CVF = 1.250199
Grid value 31:    lambda = .0177202   no. of nonzero coef. =      11
Folds: 1...5....10   CVF = 1.248975
Grid value 32:    lambda =  .016146   no. of nonzero coef. =      16
Folds: 1...5....10   CVF = 1.247768
Grid value 33:    lambda = .0147116   no. of nonzero coef. =      18
Folds: 1...5....10   CVF = 1.246598
Grid value 34:    lambda = .0134047   no. of nonzero coef. =      22
Folds: 1...5....10   CVF = 1.245454
Grid value 35:    lambda = .0122138   no. of nonzero coef. =      25
Folds: 1...5....10   CVF = 1.244336
Grid value 36:    lambda = .0111288   no. of nonzero coef. =      28
Folds: 1...5....10   CVF = 1.243234
Grid value 37:    lambda = .0101401   no. of nonzero coef. =      38
Folds: 1...5....10   CVF = 1.242221
Grid value 38:    lambda = .0092393   no. of nonzero coef. =      46
Folds: 1...5....10   CVF = 1.241315
Grid value 39:    lambda = .0084185   no. of nonzero coef. =      47
Folds: 1...5....10   CVF = 1.240499
Grid value 40:    lambda = .0076706   no. of nonzero coef. =      52
Folds: 1...5....10   CVF = 1.239915
Grid value 41:    lambda = .0069892   no. of nonzero coef. =      58
Folds: 1...5....10   CVF = 1.239514
Grid value 42:    lambda = .0063683   no. of nonzero coef. =      66
Folds: 1...5....10   CVF = 1.239295
Grid value 43:    lambda = .0058026   no. of nonzero coef. =      71
Folds: 1...5....10   CVF = 1.239239
Grid value 44:    lambda = .0052871   no. of nonzero coef. =      77
Folds: 1...5....10   CVF = 1.239371
Grid value 45:    lambda = .0048174   no. of nonzero coef. =      91
Folds: 1...5....10   CVF =  1.23965
Grid value 46:    lambda = .0043894   no. of nonzero coef. =      97
Folds: 1...5....10   CVF =  1.24005
Grid value 47:    lambda = .0039995   no. of nonzero coef. =     110
Folds: 1...5....10   CVF = 1.240475
Grid value 48:    lambda = .0036442   no. of nonzero coef. =     119
Folds: 1...5....10   CVF =  1.24096
Grid value 49:    lambda = .0033204   no. of nonzero coef. =     124
Folds: 1...5....10   CVF = 1.241512
Grid value 50:    lambda = .0030255   no. of nonzero coef. =     130
Folds: 1...5....10   CVF = 1.242104
... cross-validation complete ... minimum found

alpha 3 of 3: alpha = 0.5
note: firstvote omitted because it is constant.
note: 46.kunta19 omitted because it is constant.
note: 75.kunta19 omitted because it is constant.
note: 81.kunta19 omitted because it is constant.
note: 102.kunta19 omitted because it is constant.
note: 143.kunta19 omitted because it is constant.
note: 145.kunta19 omitted because it is constant.
note: 152.kunta19 omitted because it is constant.
note: 172.kunta19 omitted because it is constant.
note: 176.kunta19 omitted because it is constant.
note: 178.kunta19 omitted because it is constant.
note: 213.kunta19 omitted because it is constant.
note: 214.kunta19 omitted because it is constant.
note: 218.kunta19 omitted because it is constant.
note: 271.kunta19 omitted because it is constant.
note: 280.kunta19 omitted because it is constant.
note: 288.kunta19 omitted because it is constant.
note: 420.kunta19 omitted because it is constant.
note: 430.kunta19 omitted because it is constant.
note: 483.kunta19 omitted because it is constant.
note: 489.kunta19 omitted because it is constant.
note: 619.kunta19 omitted because it is constant.
note: 636.kunta19 omitted because it is constant.
note: 686.kunta19 omitted because it is constant.
note: 702.kunta19 omitted because it is constant.
note: 831.kunta19 omitted because it is constant.
note: 848.kunta19 omitted because it is constant.
note: 922.kunta19 omitted because it is constant.
note: 934.kunta19 omitted because it is constant.
note: 976.kunta19 omitted because it is constant.
note: 50.kunta19 omitted because it is constant in C.V. subsamples.
note: 78.kunta19 omitted because it is constant in C.V. subsamples.
note: 98.kunta19 omitted because it is constant in C.V. subsamples.
note: 99.kunta19 omitted because it is constant in C.V. subsamples.
note: 204.kunta19 omitted because it is constant in C.V. subsamples.
note: 232.kunta19 omitted because it is constant in C.V. subsamples.
note: 235.kunta19 omitted because it is constant in C.V. subsamples.
note: 263.kunta19 omitted because it is constant in C.V. subsamples.
note: 275.kunta19 omitted because it is constant in C.V. subsamples.
note: 284.kunta19 omitted because it is constant in C.V. subsamples.
note: 291.kunta19 omitted because it is constant in C.V. subsamples.
note: 301.kunta19 omitted because it is constant in C.V. subsamples.
note: 317.kunta19 omitted because it is constant in C.V. subsamples.
note: 320.kunta19 omitted because it is constant in C.V. subsamples.
note: 399.kunta19 omitted because it is constant in C.V. subsamples.
note: 478.kunta19 omitted because it is constant in C.V. subsamples.
note: 484.kunta19 omitted because it is constant in C.V. subsamples.
note: 560.kunta19 omitted because it is constant in C.V. subsamples.
note: 576.kunta19 omitted because it is constant in C.V. subsamples.
note: 599.kunta19 omitted because it is constant in C.V. subsamples.
note: 611.kunta19 omitted because it is constant in C.V. subsamples.
note: 631.kunta19 omitted because it is constant in C.V. subsamples.
note: 689.kunta19 omitted because it is constant in C.V. subsamples.
note: 704.kunta19 omitted because it is constant in C.V. subsamples.
note: 748.kunta19 omitted because it is constant in C.V. subsamples.
note: 751.kunta19 omitted because it is constant in C.V. subsamples.
note: 755.kunta19 omitted because it is constant in C.V. subsamples.
note: 777.kunta19 omitted because it is constant in C.V. subsamples.
note: 791.kunta19 omitted because it is constant in C.V. subsamples.
note: 832.kunta19 omitted because it is constant in C.V. subsamples.
note: 833.kunta19 omitted because it is constant in C.V. subsamples.
note: 846.kunta19 omitted because it is constant in C.V. subsamples.
note: 854.kunta19 omitted because it is constant in C.V. subsamples.
note: 859.kunta19 omitted because it is constant in C.V. subsamples.
note: 892.kunta19 omitted because it is constant in C.V. subsamples.
10-fold cross-validation with 109 lambdas ...
Grid value 1:     lambda = .2500247   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.385976
Grid value 2:     lambda = .2278132   no. of nonzero coef. =       1
Folds: 1...5....10   CVF =  1.37908
Grid value 3:     lambda = .2075749   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.371952
Grid value 4:     lambda = .1891345   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.359852
Grid value 5:     lambda = .1723324   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.348837
Grid value 6:     lambda = .1666832   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.345211
Grid value 7:     lambda = .1518755   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.334908
Grid value 8:     lambda = .1383833   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.325427
Grid value 9:     lambda = .1260897   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.317126
Grid value 10:    lambda = .1250124   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.316415
Grid value 11:    lambda = .1139066   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.308466
Grid value 12:    lambda = .1037875   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.300794
Grid value 13:    lambda = .0945673   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.293698
Grid value 14:    lambda = .0861662   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.287228
Grid value 15:    lambda = .0785114   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.281568
Grid value 16:    lambda = .0715367   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.276668
Grid value 17:    lambda = .0651816   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.272434
Grid value 18:    lambda =  .059391   no. of nonzero coef. =       7
Folds: 1...5....10   CVF =  1.26878
Grid value 19:    lambda = .0541149   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.265631
Grid value 20:    lambda = .0493075   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.262911
Grid value 21:    lambda = .0449271   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.260398
Grid value 22:    lambda = .0409359   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.257953
Grid value 23:    lambda = .0372993   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.255803
Grid value 24:    lambda = .0339857   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.253969
Grid value 25:    lambda = .0309665   no. of nonzero coef. =       9
Folds: 1...5....10   CVF = 1.252404
Grid value 26:    lambda = .0282155   no. of nonzero coef. =      11
Folds: 1...5....10   CVF = 1.251006
Grid value 27:    lambda = .0257089   no. of nonzero coef. =      12
Folds: 1...5....10   CVF = 1.249705
Grid value 28:    lambda =  .023425   no. of nonzero coef. =      16
Folds: 1...5....10   CVF = 1.248367
Grid value 29:    lambda =  .021344   no. of nonzero coef. =      19
Folds: 1...5....10   CVF = 1.247108
Grid value 30:    lambda = .0194479   no. of nonzero coef. =      25
Folds: 1...5....10   CVF = 1.245851
Grid value 31:    lambda = .0177202   no. of nonzero coef. =      25
Folds: 1...5....10   CVF = 1.244646
Grid value 32:    lambda =  .016146   no. of nonzero coef. =      32
Folds: 1...5....10   CVF = 1.243485
Grid value 33:    lambda = .0147116   no. of nonzero coef. =      42
Folds: 1...5....10   CVF = 1.242427
Grid value 34:    lambda = .0134047   no. of nonzero coef. =      46
Folds: 1...5....10   CVF = 1.241466
Grid value 35:    lambda = .0122138   no. of nonzero coef. =      49
Folds: 1...5....10   CVF = 1.240647
Grid value 36:    lambda = .0111288   no. of nonzero coef. =      55
Folds: 1...5....10   CVF = 1.240068
Grid value 37:    lambda = .0101401   no. of nonzero coef. =      59
Folds: 1...5....10   CVF = 1.239673
Grid value 38:    lambda = .0092393   no. of nonzero coef. =      66
Folds: 1...5....10   CVF = 1.239465
Grid value 39:    lambda = .0084185   no. of nonzero coef. =      73
Folds: 1...5....10   CVF =  1.23942
Grid value 40:    lambda = .0076706   no. of nonzero coef. =      84
Folds: 1...5....10   CVF =  1.23956
Grid value 41:    lambda = .0069892   no. of nonzero coef. =      92
Folds: 1...5....10   CVF = 1.239852
Grid value 42:    lambda = .0063683   no. of nonzero coef. =     106
Folds: 1...5....10   CVF = 1.240219
Grid value 43:    lambda = .0058026   no. of nonzero coef. =     113
Folds: 1...5....10   CVF = 1.240635
Grid value 44:    lambda = .0052871   no. of nonzero coef. =     122
Folds: 1...5....10   CVF = 1.241119
Grid value 45:    lambda = .0048174   no. of nonzero coef. =     126
Folds: 1...5....10   CVF = 1.241668
Grid value 46:    lambda = .0043894   no. of nonzero coef. =     132
Folds: 1...5....10   CVF = 1.242256
... cross-validation complete ... minimum found

Elastic net logit model                          No. of obs        =     14,662
                                                 No. of covariates =        203
Selection: Cross-validation                      No. of CV folds   =         10

-------------------------------------------------------------------------------
               |                               No. of      Out-of-
               |                              nonzero       sample      CV mean
alpha       ID |     Description      lambda    coef.   dev. ratio     deviance
---------------+---------------------------------------------------------------
1.000          |
             1 |    first lambda    .2500247        0      -0.0001      1.38637
            45 |   lambda before    .0048174       65       0.1061     1.239208
          * 46 | selected lambda    .0043894       69       0.1061     1.239161
            47 |    lambda after    .0039995       76       0.1060     1.239297
            52 |     last lambda    .0025118      123       0.1044     1.241488
---------------+---------------------------------------------------------------
0.750          |
            53 |    first lambda    .2500247        0      -0.0001      1.38637
           102 |     last lambda    .0030255      130       0.1040     1.242104
---------------+---------------------------------------------------------------
0.500          |
           103 |    first lambda    .2500247        0       0.0002     1.385976
           148 |     last lambda    .0043894      132       0.1039     1.242256
-------------------------------------------------------------------------------
* alpha and lambda selected by cross-validation.

. predict pvoteold_enet
(options pr penalized assumed; Pr(voted22) with penalized coefficients)

. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. gen tmun=""
(3,574,284 missing values generated)

. replace tmun=   "Alajärvi"     if kunta19==    5
variable tmun was str1 now str9
(9,532 real changes made)

. replace tmun=   "Alavieska"     if kunta19==    9
(2,519 real changes made)

. replace tmun=   "Alavus"        if kunta19==    10
(11,447 real changes made)

. replace tmun=   "Aura"  if kunta19==    19
(3,926 real changes made)

. replace tmun=   "Eurajoki"      if kunta19==    51
(9,381 real changes made)

. replace tmun=   "Forssa"        if kunta19==    61
(15,418 real changes made)

. replace tmun=   "Haapajärvi"   if kunta19==    69
variable tmun was str9 now str11
(7,002 real changes made)

. replace tmun=   "Haapavesi"     if kunta19==    71
(6,751 real changes made)

. replace tmun=   "Hailuoto"      if kunta19==    72
(958 real changes made)

. replace tmun=   "Halsua"        if kunta19==    74
(1,127 real changes made)

. replace tmun=   "Hankasalmi"    if kunta19==    77
(4,860 real changes made)

. replace tmun=   "Harjavalta"    if kunta19==    79
(6,917 real changes made)

. replace tmun=   "Hausjärvi"    if kunta19==    86
(8,230 real changes made)

. replace tmun=   "Hämeenkyrö"  if kunta19==    108
variable tmun was str11 now str12
(10,388 real changes made)

. replace tmun=   "Ilomantsi"     if kunta19==    146
(4,854 real changes made)

. replace tmun=   "Isojoki"       if kunta19==    151
(1,949 real changes made)

. replace tmun=   "Janakkala"     if kunta19==    165
(16,379 real changes made)

. replace tmun=   "Joensuu"       if kunta19==    167
(75,565 real changes made)

. replace tmun=   "Jokioinen"     if kunta19==    169
(5,128 real changes made)

. replace tmun=   "Juupajoki"     if kunta19==    177
(1,844 real changes made)

. replace tmun=   "Jyväskylä"   if kunta19==    179
(141,454 real changes made)

. replace tmun=   "Jämsä"       if kunta19==    182
(20,159 real changes made)

. replace tmun=   "Kaarina"       if kunta19==    202
(33,821 real changes made)

. replace tmun=   "Kannonkoski"   if kunta19==    216
(1,339 real changes made)

. replace tmun=   "Kannus"        if kunta19==    217
(5,456 real changes made)

. replace tmun=   "Karkkila"      if kunta19==    224
(8,701 real changes made)

. replace tmun=   "Karstula"      if kunta19==    226
(3,946 real changes made)

. replace tmun=   "Karvia"        if kunta19==    230
(2,341 real changes made)

. replace tmun=   "Kaskinen"      if kunta19==    231
(1,246 real changes made)

. replace tmun=   "Kaustinen"     if kunta19==    236
(4,258 real changes made)

. replace tmun=   "Keitele"       if kunta19==    239
(2,191 real changes made)

. replace tmun=   "Kemi"  if kunta19==    240
(20,628 real changes made)

. replace tmun=   "Kemiönsaari"  if kunta19==    322
(6,607 real changes made)

. replace tmun=   "Kempele"       if kunta19==    244
(18,337 real changes made)

. replace tmun=   "Kerava"        if kunta19==    245
(36,366 real changes made)

. replace tmun=   "Kirkkonummi"   if kunta19==    257
(39,402 real changes made)

. replace tmun=   "Kivijärvi"    if kunta19==    265
(1,095 real changes made)

. replace tmun=   "Konnevesi"     if kunta19==    275
(2,624 real changes made)

. replace tmun=   "Kontiolahti"   if kunta19==    276
(14,176 real changes made)

. replace tmun=   "Kristiinankaupunki"    if kunta19==    287
variable tmun was str12 now str18
(5,871 real changes made)

. replace tmun=   "Kuortane"      if kunta19==    300
(3,547 real changes made)

. replace tmun=   "Kustavi"       if kunta19==    304
(948 real changes made)

. replace tmun=   "Kuusamo"       if kunta19==    305
(15,097 real changes made)

. replace tmun=   "Lapinlahti"    if kunta19==    402
(9,399 real changes made)

. replace tmun=   "Lappajärvi"   if kunta19==    403
(2,973 real changes made)

. replace tmun=   "Laukaa"        if kunta19==    410
(18,885 real changes made)

. replace tmun=   "Lieksa"        if kunta19==    422
(10,843 real changes made)

. replace tmun=   "Liminka"       if kunta19==    425
(10,183 real changes made)

. replace tmun=   "Liperi"        if kunta19==    426
(12,070 real changes made)

. replace tmun=   "Lohja" if kunta19==    444
(45,890 real changes made)

. replace tmun=   "Lumijoki"      if kunta19==    436
(2,020 real changes made)

. replace tmun=   "Luumäki"      if kunta19==    441
(4,627 real changes made)

. replace tmun=   "Maalahti"      if kunta19==    475
(4,893 real changes made)

. replace tmun=   "Masku" if kunta19==    481
(9,522 real changes made)

. replace tmun=   "Muhos" if kunta19==    494
(7,886 real changes made)

. replace tmun=   "Muurame"       if kunta19==    500
(10,158 real changes made)

. replace tmun=   "Mynämäki"    if kunta19==    503
(7,644 real changes made)

. replace tmun=   "Mäntyharju"   if kunta19==    507
(5,696 real changes made)

. replace tmun=   "Nakkila"       if kunta19==    531
(5,322 real changes made)

. replace tmun=   "Nivala"        if kunta19==    535
(10,630 real changes made)

. replace tmun=   "Nurmijärvi"   if kunta19==    543
(42,873 real changes made)

. replace tmun=   "Oulu"  if kunta19==    564
(204,869 real changes made)

. replace tmun=   "Outokumpu"     if kunta19==    309
(6,664 real changes made)

. replace tmun=   "Paimio"        if kunta19==    577
(10,836 real changes made)

. replace tmun=   "Paltamo"       if kunta19==    578
(2,914 real changes made)

. replace tmun=   "Parainen"      if kunta19==    445
(15,104 real changes made)

. replace tmun=   "Parkano"       if kunta19==    581
(6,391 real changes made)

. replace tmun=   "Pelkosenniemi" if kunta19==    583
(936 real changes made)

. replace tmun=   "Perho" if kunta19==    584
(2,758 real changes made)

. replace tmun=   "Pertunmaa"     if kunta19==    588
(1,684 real changes made)

. replace tmun=   "Petäjävesi"  if kunta19==    592
(3,838 real changes made)

. replace tmun=   "Pieksämäki"  if kunta19==    593
(17,649 real changes made)

. replace tmun=   "Polvijärvi"   if kunta19==    607
(4,238 real changes made)

. replace tmun=   "Pomarkku"      if kunta19==    608
(2,089 real changes made)

. replace tmun=   "Pori"  if kunta19==    609
(79,217 real changes made)

. replace tmun=   "Porvoo"        if kunta19==    638
(49,489 real changes made)

. replace tmun=   "Posio" if kunta19==    614
(3,177 real changes made)

. replace tmun=   "Puolanka"      if kunta19==    620
(2,527 real changes made)

. replace tmun=   "Puumala"       if kunta19==    623
(2,118 real changes made)

. replace tmun=   "Pyhtää"      if kunta19==    624
(5,120 real changes made)

. replace tmun=   "Pyhäjärvi"   if kunta19==    626
(5,122 real changes made)

. replace tmun=   "Pyhäranta"    if kunta19==    631
(1,995 real changes made)

. replace tmun=   "Pälkäne"     if kunta19==    635
(6,431 real changes made)

. replace tmun=   "Raisio"        if kunta19==    680
(23,945 real changes made)

. replace tmun=   "Ranua" if kunta19==    683
(3,782 real changes made)

. replace tmun=   "Rautavaara"    if kunta19==    687
(1,575 real changes made)

. replace tmun=   "Reisjärvi"    if kunta19==    691
(2,716 real changes made)

. replace tmun=   "Ristijärvi"   if kunta19==    697
(1,248 real changes made)

. replace tmun=   "Rovaniemi"     if kunta19==    698
(62,855 real changes made)

. replace tmun=   "Saarijärvi"   if kunta19==    729
(9,290 real changes made)

. replace tmun=   "Salo"  if kunta19==    734
(51,690 real changes made)

. replace tmun=   "Sievi" if kunta19==    746
(4,862 real changes made)

. replace tmun=   "Siikainen"     if kunta19==    747
(1,436 real changes made)

. replace tmun=   "Sipoo" if kunta19==    753
(21,111 real changes made)

. replace tmun=   "Soini" if kunta19==    759
(2,050 real changes made)

. replace tmun=   "Somero"        if kunta19==    761
(8,698 real changes made)

. replace tmun=   "Sonkajärvi"   if kunta19==    762
(3,363 real changes made)

. replace tmun=   "Sotkamo"       if kunta19==    765
(10,312 real changes made)

. replace tmun=   "Sulkava"       if kunta19==    768
(2,316 real changes made)

. replace tmun=   "Sysmä"        if kunta19==    781
(3,654 real changes made)

. replace tmun=   "Tampere"       if kunta19==    837
(237,132 real changes made)

. replace tmun=   "Tervola"       if kunta19==    845
(2,974 real changes made)

. replace tmun=   "Toholampi"     if kunta19==    849
(2,776 real changes made)

. replace tmun=   "Toivakka"      if kunta19==    850
(2,387 real changes made)

. replace tmun=   "Utajärvi"     if kunta19==    889
(2,660 real changes made)

. replace tmun=   "Uusikaupunki"  if kunta19==    895
(15,441 real changes made)

. replace tmun=   "Vaala" if kunta19==    785
(2,789 real changes made)

. replace tmun=   "Vaasa" if kunta19==    905
(67,341 real changes made)

. replace tmun=   "Valkeakoski"   if kunta19==    908
(20,919 real changes made)

. replace tmun=   "Vantaa"        if kunta19==    92
(231,843 real changes made)

. replace tmun=   "Vieremä"      if kunta19==    925
(3,570 real changes made)

. replace tmun=   "Vihti" if kunta19==    927
(29,111 real changes made)

. replace tmun=   "Viitasaari"    if kunta19==    931
(5,689 real changes made)

. replace tmun=   "Virolahti"     if kunta19==    935
(2,790 real changes made)

. replace tmun=   "Vöyri"        if kunta19==    946
(6,438 real changes made)

. replace tmun=   "Ylivieska"     if kunta19==    977
(15,231 real changes made)

. replace tmun=   "Ypäjä"       if kunta19==    981
(2,337 real changes made)

. replace tmun=   "Ähtäri"      if kunta19==    989
(5,610 real changes made)

. 
. gen treatedmun=tmun!=""

. 
. gen shrereg=.
(3,574,284 missing values generated)

. 
. replace shrereg =       100     if kunta19==    5
(9,532 real changes made)

. replace shrereg =       100     if kunta19==    9
(2,519 real changes made)

. replace shrereg =       100     if kunta19==    10
(11,447 real changes made)

. replace shrereg =       100     if kunta19==    19
(3,926 real changes made)

. replace shrereg =       100     if kunta19==    51
(9,381 real changes made)

. replace shrereg =       80.8    if kunta19==    61
(15,418 real changes made)

. replace shrereg =       100     if kunta19==    69
(7,002 real changes made)

. replace shrereg =       100     if kunta19==    71
(6,751 real changes made)

. replace shrereg =       100     if kunta19==    72
(958 real changes made)

. replace shrereg =       100     if kunta19==    74
(1,127 real changes made)

. replace shrereg =       100     if kunta19==    77
(4,860 real changes made)

. replace shrereg =       100     if kunta19==    79
(6,917 real changes made)

. replace shrereg =       100     if kunta19==    86
(8,230 real changes made)

. replace shrereg =       100     if kunta19==    108
(10,388 real changes made)

. replace shrereg =       100     if kunta19==    146
(4,854 real changes made)

. replace shrereg =       100     if kunta19==    151
(1,949 real changes made)

. replace shrereg =       100     if kunta19==    165
(16,379 real changes made)

. replace shrereg =       97.7    if kunta19==    167
(75,565 real changes made)

. replace shrereg =       100     if kunta19==    169
(5,128 real changes made)

. replace shrereg =       100     if kunta19==    177
(1,844 real changes made)

. replace shrereg =       99.2    if kunta19==    179
(141,454 real changes made)

. replace shrereg =       100     if kunta19==    182
(20,159 real changes made)

. replace shrereg =       100     if kunta19==    202
(33,821 real changes made)

. replace shrereg =       100     if kunta19==    216
(1,339 real changes made)

. replace shrereg =       100     if kunta19==    217
(5,456 real changes made)

. replace shrereg =       100     if kunta19==    224
(8,701 real changes made)

. replace shrereg =       100     if kunta19==    226
(3,946 real changes made)

. replace shrereg =       100     if kunta19==    230
(2,341 real changes made)

. replace shrereg =       100     if kunta19==    231
(1,246 real changes made)

. replace shrereg =       100     if kunta19==    236
(4,258 real changes made)

. replace shrereg =       100     if kunta19==    239
(2,191 real changes made)

. replace shrereg =       100     if kunta19==    240
(20,628 real changes made)

. replace shrereg =       100     if kunta19==    322
(6,607 real changes made)

. replace shrereg =       100     if kunta19==    244
(18,337 real changes made)

. replace shrereg =       100     if kunta19==    245
(36,366 real changes made)

. replace shrereg =       100     if kunta19==    257
(39,402 real changes made)

. replace shrereg =       100     if kunta19==    265
(1,095 real changes made)

. replace shrereg =       100     if kunta19==    275
(2,624 real changes made)

. replace shrereg =       83.4    if kunta19==    276
(14,176 real changes made)

. replace shrereg =       80.5    if kunta19==    287
(5,871 real changes made)

. replace shrereg =       100     if kunta19==    300
(3,547 real changes made)

. replace shrereg =       100     if kunta19==    304
(948 real changes made)

. replace shrereg =       100     if kunta19==    305
(15,097 real changes made)

. replace shrereg =       100     if kunta19==    402
(9,399 real changes made)

. replace shrereg =       100     if kunta19==    403
(2,973 real changes made)

. replace shrereg =       100     if kunta19==    410
(18,885 real changes made)

. replace shrereg =       100     if kunta19==    422
(10,843 real changes made)

. replace shrereg =       100     if kunta19==    425
(10,183 real changes made)

. replace shrereg =       100     if kunta19==    426
(12,070 real changes made)

. replace shrereg =       100     if kunta19==    444
(45,890 real changes made)

. replace shrereg =       100     if kunta19==    436
(2,020 real changes made)

. replace shrereg =       100     if kunta19==    441
(4,627 real changes made)

. replace shrereg =       83.8    if kunta19==    475
(4,893 real changes made)

. replace shrereg =       100     if kunta19==    481
(9,522 real changes made)

. replace shrereg =       80.7    if kunta19==    494
(7,886 real changes made)

. replace shrereg =       100     if kunta19==    500
(10,158 real changes made)

. replace shrereg =       100     if kunta19==    503
(7,644 real changes made)

. replace shrereg =       100     if kunta19==    507
(5,696 real changes made)

. replace shrereg =       100     if kunta19==    531
(5,322 real changes made)

. replace shrereg =       100     if kunta19==    535
(10,630 real changes made)

. replace shrereg =       100     if kunta19==    543
(42,873 real changes made)

. replace shrereg =       100     if kunta19==    564
(204,869 real changes made)

. replace shrereg =       100     if kunta19==    309
(6,664 real changes made)

. replace shrereg =       100     if kunta19==    577
(10,836 real changes made)

. replace shrereg =       83.4    if kunta19==    578
(2,914 real changes made)

. replace shrereg =       100     if kunta19==    445
(15,104 real changes made)

. replace shrereg =       100     if kunta19==    581
(6,391 real changes made)

. replace shrereg =       100     if kunta19==    583
(936 real changes made)

. replace shrereg =       100     if kunta19==    584
(2,758 real changes made)

. replace shrereg =       100     if kunta19==    588
(1,684 real changes made)

. replace shrereg =       100     if kunta19==    592
(3,838 real changes made)

. replace shrereg =       100     if kunta19==    593
(17,649 real changes made)

. replace shrereg =       84.3    if kunta19==    607
(4,238 real changes made)

. replace shrereg =       100     if kunta19==    608
(2,089 real changes made)

. replace shrereg =       90.7    if kunta19==    609
(79,217 real changes made)

. replace shrereg =       97.6    if kunta19==    638
(49,489 real changes made)

. replace shrereg =       100     if kunta19==    614
(3,177 real changes made)

. replace shrereg =       100     if kunta19==    620
(2,527 real changes made)

. replace shrereg =       100     if kunta19==    623
(2,118 real changes made)

. replace shrereg =       100     if kunta19==    624
(5,120 real changes made)

. replace shrereg =       100     if kunta19==    626
(5,122 real changes made)

. replace shrereg =       100     if kunta19==    631
(1,995 real changes made)

. replace shrereg =       100     if kunta19==    635
(6,431 real changes made)

. replace shrereg =       100     if kunta19==    680
(23,945 real changes made)

. replace shrereg =       100     if kunta19==    683
(3,782 real changes made)

. replace shrereg =       100     if kunta19==    687
(1,575 real changes made)

. replace shrereg =       100     if kunta19==    691
(2,716 real changes made)

. replace shrereg =       100     if kunta19==    697
(1,248 real changes made)

. replace shrereg =       100     if kunta19==    698
(62,855 real changes made)

. replace shrereg =       100     if kunta19==    729
(9,290 real changes made)

. replace shrereg =       90.1    if kunta19==    734
(51,690 real changes made)

. replace shrereg =       100     if kunta19==    746
(4,862 real changes made)

. replace shrereg =       100     if kunta19==    747
(1,436 real changes made)

. replace shrereg =       100     if kunta19==    753
(21,111 real changes made)

. replace shrereg =       100     if kunta19==    759
(2,050 real changes made)

. replace shrereg =       100     if kunta19==    761
(8,698 real changes made)

. replace shrereg =       81.3    if kunta19==    762
(3,363 real changes made)

. replace shrereg =       100     if kunta19==    765
(10,312 real changes made)

. replace shrereg =       88.2    if kunta19==    768
(2,316 real changes made)

. replace shrereg =       100     if kunta19==    781
(3,654 real changes made)

. replace shrereg =       100     if kunta19==    837
(237,132 real changes made)

. replace shrereg =       100     if kunta19==    845
(2,974 real changes made)

. replace shrereg =       84.9    if kunta19==    849
(2,776 real changes made)

. replace shrereg =       100     if kunta19==    850
(2,387 real changes made)

. replace shrereg =       100     if kunta19==    889
(2,660 real changes made)

. replace shrereg =       95.9    if kunta19==    895
(15,441 real changes made)

. replace shrereg =       100     if kunta19==    785
(2,789 real changes made)

. replace shrereg =       100     if kunta19==    905
(67,341 real changes made)

. replace shrereg =       87.7    if kunta19==    908
(20,919 real changes made)

. replace shrereg =       100     if kunta19==    92
(231,843 real changes made)

. replace shrereg =       100     if kunta19==    925
(3,570 real changes made)

. replace shrereg =       100     if kunta19==    927
(29,111 real changes made)

. replace shrereg =       85.7    if kunta19==    931
(5,689 real changes made)

. replace shrereg =       85.3    if kunta19==    935
(2,790 real changes made)

. replace shrereg =       100     if kunta19==    946
(6,438 real changes made)

. replace shrereg =       100     if kunta19==    977
(15,231 real changes made)

. replace shrereg =       100     if kunta19==    981
(2,337 real changes made)

. replace shrereg =       100     if kunta19==    989
(5,610 real changes made)

. 
. 
. 
. save "$home\data\dataforanalysis.dta", replace
file W:\dofiles\replication\data\dataforanalysis.dta saved

. 
end of do-file

. do "$home\dofiles\Table1.do"

. ****************************************************************************************************************
> ****
. *****Do-file for Table 1
. *****Who is mobilized to vote by short text messages? Evidence from a nationwide field experiment with young vot
> ers
. ****************************************************************************************************************
> ****
. *****Last edited 24/6/5
. ****************************************************************************************************************
> ****
. *****Ado packages needed: estout
. ****************************************************************************************************************
> ****
. 
. 
. *Use data
. use \data\dataforanalysis230522_v2.dta, clear

. 
. *************************************************
. *****TABLE 1
. *************************************************
. 
. replace age=age+3 //replace age for age in 2022
(3,562,582 real changes made)

. 
. rename svatva_k income

. eststo s1: estpost tabstat female age mohighschool income foreign if treated!=.,statistics(mean sd) columns(stat
> istics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .4042974   .4907603 
         age |  24.62268   3.147517 
mohighschool |  .4383476   .4961893 
      income |  15780.93   13163.32 
     foreign |  .0407299   .1976659 

. keep if age>18 & age<30
(3,078,242 observations deleted)

. 
. 
. eststo s2: estpost tabstat female age mohighschool income foreign if treated!=.,statistics(mean sd) columns(stat
> istics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |   .406712   .4912251 
         age |  24.64912   3.125797 
mohighschool |  .4408056   .4964886 
      income |  15807.74   13160.31 
     foreign |  .0407344   .1976763 

. eststo s3: estpost tabstat female age mohighschool income foreign if shrereg!=., statistics(mean sd) columns(sta
> tistics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .4835988   .4997318 
         age |   24.1865   3.152723 
mohighschool |  .4438231    .496835 
      income |  13538.94   12398.94 
     foreign |  .0711079   .2570055 

. eststo s4: estpost tabstat female age mohighschool income foreign, statistics(mean sd) columns(statistics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .4921418   .4999387 
         age |  24.27642   3.115376 
mohighschool |  .4495446   .4974482 
      income |   13971.5   12552.65 
     foreign |   .067458   .2508138 

. 
. 
. esttab, cells(mean(fmt(2)) sd(fmt(2) par)) unstack nonote nonumber noobs stats(N, fmt(a) labels("Observations"))
>  mtitles("Sample" "Sample Municipalities" "All Voters") replace

----------------------------------------------------------------
                       s1           s2           s3           s4
                  mean/sd      mean/sd      mean/sd      mean/sd
----------------------------------------------------------------
female               0.40         0.41         0.48         0.49
                   (0.49)       (0.49)       (0.50)       (0.50)
age                 24.62        24.65        24.19        24.28
                   (3.15)       (3.13)       (3.15)       (3.12)
mohighschool         0.44         0.44         0.44         0.45
                   (0.50)       (0.50)       (0.50)       (0.50)
income           15780.93     15807.74     13538.94     13971.50
               (13163.32)   (13160.31)   (12398.94)   (12552.65)
foreign              0.04         0.04         0.07         0.07
                   (0.20)       (0.20)       (0.26)       (0.25)
----------------------------------------------------------------
Observations        51101        50596       280925       496042
----------------------------------------------------------------

. 
. 
. 
. 
. 
end of do-file

. do "$home\dofiles\Tables3-5_7-9_A1-4_and_A10.do"

. ****************************************************************************************************************
> ****
. *****Do-file for Tables 3-5, A1-4 and A10
. *****Who is mobilized to vote by short text messages? Evidence from a nationwide field experiment with young vot
> ers
. ****************************************************************************************************************
> ****
. *****Last edited 24/6/5
. ****************************************************************************************************************
> ****
. *****Ado packages needed: estout
. ****************************************************************************************************************
> ****
. 
. clear all

. 
. *Programs to calculate group differences with standard errors and stars
. capture program drop, myrepost

. program myrepost, eclass
  1. ereturn repost b=`1'
  2. ereturn repost V=`2'
  3. ereturn scalar df_r=`3'
  4. end

. 
. capture program drop mystars

. program mystars, eclass
  1. local d_stars=string(`1', "%9.3f")
  2. local pval=ttail(`3',abs(`1'/`2'))*2
  3. 
. if `pval'<=0.01 {
  4. local d_stars="`d_stars'"+"***"         
  5. }
  6. 
. if `pval'>0.01 & `pval'<=0.05  {
  7. local d_stars="`d_stars'"+"**"          
  8. }
  9. 
. if `pval'>0.05 & `pval'<=0.1  {
 10. local d_stars="`d_stars'"+"*"           
 11. }
 12. 
. local se_stars=string(`2', "%9.3f")
 13. 
. local se_stars="("+"`se_stars'"+")"
 14. 
. ereturn local d_stars="`d_stars'"
 15. ereturn local se_stars="`se_stars'"
 16. 
. 
. end

. 
. *Use data
. use \data\dataforanalysis230522_v2.dta, clear

. *Set basecategory
. fvset base 8 moses1d

. *Create variable for rural/urban municipality
. destring kuntaryhm, replace
kuntaryhm: all characters numeric; replaced as byte
(11702 missing values generated)

. gen rural=kuntaryhm==3 if kuntaryhm!=.
(11,702 missing values generated)

. replace rural=. if shrereg==.
(1,440,176 real changes made, 1,440,176 to missing)

. 
. 
. label variable treated "Treatments Pooled"

. label variable treated1 "Neutral Treatment"

. label variable treated2 "Expressive Treatment"

. label variable treated3 "Informative Treatment"

. 
. 
. 
. 
. 
. *************************************************
. *****TABLE 3
. *************************************************
. eststo clear

. eststo M1: reg voted22 treated, cluster(kunta19)

Linear regression                               Number of obs     =     50,140
                                                F(1, 289)         =       7.02
                                                Prob > F          =     0.0085
                                                R-squared         =     0.0001
                                                Root MSE          =      .4635

                              (Std. err. adjusted for 290 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     treated |   .0086995   .0032826     2.65   0.008     .0022387    .0151603
       _cons |   .3072667   .0131307    23.40   0.000     .2814228    .3331107
------------------------------------------------------------------------------

. estadd local Controls "No"

added macro:
           e(Controls) : "No"

. estadd local Controls1 " "

added macro:
          e(Controls1) : " "

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                         Number of obs = 20,064

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3072667   .0032572      .3008824    .3136511
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M1

. eststo M2: reg voted22 treated molincome female age foreign, cluster(kunta19)

Linear regression                               Number of obs     =     49,679
                                                F(5, 289)         =     395.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0238
                                                Root MSE          =     .45846

                              (Std. err. adjusted for 290 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     treated |   .0086866   .0034066     2.55   0.011     .0019816    .0153915
   molincome |   .0201752   .0036814     5.48   0.000     .0129294    .0274209
      female |   .1142324   .0066523    17.17   0.000     .1011393    .1273255
         age |   .0035524   .0014193     2.50   0.013     .0007589    .0063459
     foreign |  -.1998179    .010023   -19.94   0.000    -.2195452   -.1800906
       _cons |   -.010801   .0409913    -0.26   0.792    -.0914804    .0698783
------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income"

added macro:
          e(Controls1) : "Ln Income"

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                         Number of obs = 19,879

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3083656   .0032756      .3019453     .314786
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M2

. eststo M3:  reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(kunta19
> )

Linear regression                               Number of obs     =     49,679
                                                F(15, 289)        =     303.74
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0622
                                                Root MSE          =     .44939

                                (Std. err. adjusted for 290 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0090149   .0033085     2.72   0.007     .0025032    .0155267
     molincome |   .0026971   .0038687     0.70   0.486    -.0049173    .0103114
        female |   .1044478   .0064625    16.16   0.000     .0917282    .1171674
           age |   .0075646   .0010094     7.49   0.000     .0055779    .0095513
       foreign |  -.1427179   .0087355   -16.34   0.000    -.1599111   -.1255246
               |
       moses1d |
            1  |   .1897906   .0226286     8.39   0.000     .1452528    .2343283
            2  |   .0721931   .0146617     4.92   0.000     .0433358    .1010504
            3  |   .1282275   .0102864    12.47   0.000     .1079819    .1484732
            4  |    .045854   .0075732     6.05   0.000     .0309485    .0607596
            5  |    .005741    .009352     0.61   0.540    -.0126658    .0241477
            6  |   .0720807   .0117937     6.11   0.000     .0488683    .0952931
            7  |   .0707205   .0129955     5.44   0.000     .0451428    .0962983
            9  |   .0071824   .0140908     0.51   0.611    -.0205513     .034916
               |
     firstvote |   .1234276   .0110779    11.14   0.000      .101624    .1452311
1.mohighschool |   .1319606   .0073446    17.97   0.000     .1175049    .1464162
         _cons |  -.0370493   .0395638    -0.94   0.350     -.114919    .0408205
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                         Number of obs = 19,879

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3083656   .0032756      .3019453     .314786
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M3

. eststo M4:  reg voted22 treated i.kunta19, cluster(kunta19)

Linear regression                               Number of obs     =     50,140
                                                F(0, 289)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0215
                                                Root MSE          =     .45984

                              (Std. err. adjusted for 290 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     treated |   .0087457   .0033929     2.58   0.010     .0020678    .0154237
             |
     kunta19 |
          9  |   .2265601   .0001408  1608.77   0.000     .2262829    .2268373
         10  |    .041264   .0001167   353.54   0.000     .0410343    .0414937
         16  |   .2319351   .0019444   119.28   0.000     .2281081     .235762
         18  |   .0579803    .000883    65.66   0.000     .0562423    .0597183
         19  |   .0167563   .0001899    88.26   0.000     .0163826      .01713
         20  |  -.1414327   .0004692  -301.42   0.000    -.1423562   -.1405091
         43  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
         46  |   .2319351   .0019444   119.28   0.000     .2281081     .235762
         47  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
         49  |   .0561603   .0002023   277.58   0.000     .0557621    .0565585
         50  |   .1555089   5.59e-06  2.8e+04   0.000     .1554979    .1555199
         51  |   .0393609   .0001596   246.64   0.000     .0390468     .039675
         52  |  -.0246242   .0006003   -41.02   0.000    -.0258057   -.0234428
         60  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
         61  |  -.0399791   .0000913  -437.66   0.000    -.0401588   -.0397993
         69  |   .0474093   .0001042   455.20   0.000     .0472043    .0476143
         71  |    .080682   .0000183  4410.69   0.000      .080646     .080718
         72  |   .0349181   .0001174   297.31   0.000      .034687    .0351493
         74  |   .1186764   .0001742   681.32   0.000     .1183336    .1190192
         75  |  -.0433505   .0004045  -107.16   0.000    -.0441467   -.0425542
         77  |  -.0069955   .0002271   -30.81   0.000    -.0074425   -.0065486
         78  |  -.0715632   .0005872  -121.86   0.000     -.072719   -.0704074
         79  |  -.0750669    .000073 -1028.20   0.000    -.0752106   -.0749232
         81  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
         82  |  -.1072288   .0003175  -337.68   0.000    -.1078538   -.1066038
         86  |   .0053992   .0001435    37.61   0.000     .0051167    .0056817
         90  |  -.2724378   .0002479 -1098.79   0.000    -.2729258   -.2719498
         91  |   .0962545   .0001098   876.53   0.000     .0960384    .0964707
         92  |  -.0306877   .0000679  -452.23   0.000    -.0308213   -.0305542
         97  |   .2275622   .0002479   917.80   0.000     .2270742    .2280502
         98  |    .090801   .0000937   968.85   0.000     .0906166    .0909855
         99  |  -.0202514   .0010962   -18.47   0.000    -.0224089   -.0180939
        102  |  -.0001081   .0000937    -1.15   0.250    -.0002925    .0000764
        103  |   .2231893   .0014485   154.08   0.000     .2203383    .2260403
        105  |  -.2724378   .0002479 -1098.79   0.000    -.2729258   -.2719498
        106  |   -.004996   .0002479   -20.15   0.000     -.005484   -.0045079
        108  |   .0084174   .0000445   188.95   0.000     .0083298    .0085051
        109  |   .0325711   .0000359   907.61   0.000     .0325005    .0326418
        111  |   -.042005   .0001174  -357.65   0.000    -.0422361   -.0417738
        139  |   .0704923   .0000701  1004.99   0.000     .0703542    .0706303
        140  |  -.1239668   .0000675 -1837.37   0.000    -.1240996    -.123834
        142  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
        143  |   .1989221   .0005473   363.45   0.000     .1978449    .1999993
        145  |   .0126518   5.59e-06  2262.19   0.000     .0126408    .0126628
        146  |  -.0640299   .0002446  -261.80   0.000    -.0645113   -.0635486
        148  |  -.2066457   .0000913 -2262.19   0.000    -.2068255   -.2064659
        149  |   .2231893   .0014485   154.08   0.000     .2203383    .2260403
        151  |   .0865127   .0001592   543.40   0.000     .0861993    .0868261
        152  |  -.1327041   .0009638  -137.69   0.000    -.1346011   -.1308072
        153  |   .1159652   .0000594  1950.68   0.000     .1158482    .1160822
        165  |  -.0314948   .0000858  -367.13   0.000    -.0316636   -.0313259
        167  |   .0383592   .0001084   353.92   0.000     .0381459    .0385725
        169  |   .0123635   .0001063   116.35   0.000     .0121543    .0125726
        170  |   .7231893   .0014485   499.26   0.000     .7203383    .7260403
        171  |   .0623531   .0008134    76.65   0.000     .0607521    .0639541
        172  |  -.1618126   .0000594 -2721.89   0.000    -.1619296   -.1616956
        176  |  -.0733124   .0000913  -802.56   0.000    -.0734922   -.0731326
        177  |  -.1349591   .0000724 -1862.83   0.000    -.1351017   -.1348165
        178  |   .1249385   .0007699   162.27   0.000     .1234231    .1264539
        179  |   .0743805    .000076   978.52   0.000     .0742309    .0745301
        181  |  -.2746242   .0006003  -457.49   0.000    -.2758057   -.2734428
        182  |  -.0611779   .0000862  -709.66   0.000    -.0613476   -.0610082
        186  |  -.0736622   .0002271  -324.41   0.000    -.0741091   -.0732153
        202  |   .0296369    .000096   308.77   0.000      .029448    .0298259
        204  |   .0992825   .0010244    96.92   0.000     .0972663    .1012988
        205  |   .0009821   .0001839     5.34   0.000     .0006201    .0013441
        208  |   .0602708   5.59e-06  1.1e+04   0.000     .0602598    .0602818
        211  |  -.0005056   .0000605    -8.36   0.000    -.0006247   -.0003865
        213  |  -.2738954   .0003175  -862.55   0.000    -.2745204   -.2732704
        214  |   .0349181   .0001174   297.31   0.000      .034687    .0351493
        216  |  -.0345508   .0001672  -206.69   0.000    -.0348798   -.0342218
        217  |   .0491554   .0001351   363.77   0.000     .0488895    .0494214
        218  |  -.2746242   .0006003  -457.49   0.000    -.2758057   -.2734428
        224  |   .0178814   .0001677   106.60   0.000     .0175513    .0182116
        226  |    .027766   .0001404   197.76   0.000     .0274896    .0280423
        230  |   .1385552   .0000514  2693.99   0.000      .138454    .1386564
        231  |  -.0733124   .0000913  -802.56   0.000    -.0734922   -.0731326
        232  |  -.0507015   .0000594  -852.86   0.000    -.0508185   -.0505845
        233  |  -.0090498   .0003372   -26.84   0.000    -.0097135    -.008386
        235  |   .0594379   .0003175   187.18   0.000     .0588129    .0600629
        236  |   .0724166   .0001167   620.45   0.000     .0721869    .0726463
        239  |   .1385552   .0000514  2693.99   0.000      .138454    .1386564
        240  |  -.0185841   .0000969  -191.86   0.000    -.0187747   -.0183934
        241  |  -.2200364   .0001587 -1386.87   0.000    -.2203487   -.2197241
        244  |   .0803108   .0000913   879.18   0.000      .080131    .0804906
        245  |  -.0168865   .0000411  -411.04   0.000    -.0169674   -.0168057
        249  |  -.1545335   .0003477  -444.40   0.000    -.1552179   -.1538491
        250  |  -.0246242   .0006003   -41.02   0.000    -.0258057   -.0234428
        256  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
        257  |    .001725   .0001584    10.89   0.000     .0014133    .0020367
        260  |   -.138813    .000361  -384.48   0.000    -.1395236   -.1381023
        261  |   .0275622   .0002479   111.16   0.000     .0270742    .0280502
        263  |  -.0066457   .0000913   -72.75   0.000    -.0068255   -.0064659
        265  |   .0118189   .0003175    37.22   0.000     .0111939    .0124438
        271  |   -.023531   .0001762  -133.57   0.000    -.0238778   -.0231843
        272  |   .0051006   .0000323   157.71   0.000     .0050369    .0051642
        273  |  -.0733124   .0000913  -802.56   0.000    -.0734922   -.0731326
        275  |  -.1086864    .000883  -123.08   0.000    -.1104244   -.1069484
        276  |    .038711   .0000386  1001.82   0.000     .0386349     .038787
        280  |   .5284368   .0005872   899.87   0.000      .527281    .5295926
        284  |  -.2724378   .0002479 -1098.79   0.000    -.2729258   -.2719498
        285  |  -.0095325   .0004506   -21.16   0.000    -.0104194   -.0086457
        286  |   .0263961   .0002044   129.11   0.000     .0259937    .0267985
        287  |   .1797306   .0001232  1459.37   0.000     .1794882     .179973
        288  |   .1266876   .0000913  1386.87   0.000     .1265078    .1268674
        290  |   .3927712   .0003175  1236.91   0.000     .3921462    .3933962
        291  |   .3927712   .0003175  1236.91   0.000     .3921462    .3933962
        297  |   .1275901   .0000265  4817.78   0.000      .127538    .1276422
        300  |   .0360448    .000156   231.09   0.000     .0357378    .0363518
        301  |  -.0249887   .0007417   -33.69   0.000    -.0264484   -.0235289
        304  |   .0594379   .0003175   187.18   0.000     .0588129    .0600629
        305  |  -.0966677   .0001791  -539.87   0.000    -.0970201   -.0963153
        309  |  -.0971064    .000194  -500.57   0.000    -.0974882   -.0967246
        312  |  -.2730625   5.59e-06 -4.9e+04   0.000    -.2730735   -.2730515
        316  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
        317  |  -.0516732   .0003175  -162.73   0.000    -.0522982   -.0510482
        320  |  -.2759361   .0011092  -248.77   0.000    -.2781193   -.2737529
        322  |  -.0500044    .000055  -909.25   0.000    -.0501126   -.0498962
        398  |  -.0187301   .0001209  -154.98   0.000    -.0189679   -.0184922
        399  |  -.1733124   .0000913 -1897.28   0.000    -.1734922   -.1731326
        400  |  -.0838446   .0006721  -124.76   0.000    -.0851673   -.0825218
        402  |  -.0720423   .0000445 -1617.15   0.000      -.07213   -.0719546
        403  |   .1264933   .0001667   758.60   0.000     .1261651    .1268215
        405  |   .1495798   .0000417  3587.45   0.000     .1494978    .1496619
        407  |  -.2724378   .0002479 -1098.79   0.000    -.2729258   -.2719498
        408  |  -.0451651   .0002479  -182.16   0.000    -.0456531   -.0446771
        410  |  -.0165882   .0000304  -544.78   0.000    -.0166481   -.0165282
        416  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
        418  |   .0638048   .0003029   210.68   0.000     .0632087    .0644008
        420  |   .1284368   .0005872   218.71   0.000      .127281    .1295926
        422  |  -.0583715   .0001351  -431.97   0.000    -.0586374   -.0581055
        423  |   .1766876   .0000913  1934.23   0.000     .1765078    .1768674
        425  |   .0973561   .0000398  2443.18   0.000     .0972776    .0974345
        426  |   -.025695   .0001574  -163.24   0.000    -.0260049   -.0253852
        430  |   .0132765   .0002479    53.55   0.000     .0127885    .0137645
        433  |  -.1057711   .0002479  -426.59   0.000    -.1062592   -.1052831
        434  |  -.0733124   .0000913  -802.56   0.000    -.0734922   -.0731326
        436  |   .1443924   .0002764   522.37   0.000     .1438483    .1449364
        440  |   .1231893   .0014485    85.05   0.000     .1203383    .1260403
        441  |   .1838305   .0000913  2012.42   0.000     .1836507    .1840103
        444  |  -.0446307   .0000897  -497.72   0.000    -.0448072   -.0444542
        445  |   .0844376     .00011   767.44   0.000     .0842211    .0846542
        475  |   .1863125   .0000272  6860.45   0.000     .1862591     .186366
        480  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
        481  |   .0282472   .0001021   276.61   0.000     .0280462    .0284481
        483  |   .2297486   .0010962   209.59   0.000     .2275911    .2319061
        484  |  -.1277066    .000975  -130.98   0.000    -.1296256   -.1257876
        489  |   .2319351   .0019444   119.28   0.000     .2281081     .235762
        491  |   .0117594   .0003406    34.52   0.000     .0110889    .0124298
        494  |   .1224972   .0000201  6106.09   0.000     .1224577    .1225367
        495  |   .2231893   .0014485   154.08   0.000     .2203383    .2260403
        498  |   .1555089   5.59e-06  2.8e+04   0.000     .1554979    .1555199
        499  |  -.0295491   .0001104  -267.67   0.000    -.0297664   -.0293318
        500  |   .0683894   .0000576  1186.71   0.000      .068276    .0685029
        503  |  -.0716007    .000053 -1349.92   0.000    -.0717051   -.0714963
        505  |   .0275622   .0002479   111.16   0.000     .0270742    .0280502
        507  |  -.0100624   .0000556  -180.87   0.000    -.0101719   -.0099529
        508  |  -.0312093   .0001894  -164.74   0.000    -.0315822   -.0308364
        529  |  -.0413322   .0003784  -109.22   0.000    -.0420771   -.0405874
        531  |  -.0810551   .0001109  -730.74   0.000    -.0812735   -.0808368
        535  |   .1192787   .0001163  1025.90   0.000     .1190499    .1195076
        536  |  -.0727742   .0001174  -619.63   0.000    -.0730053    -.072543
        538  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
        541  |  -.0721463    .000361  -199.83   0.000    -.0728569   -.0714357
        543  |   .0065307   .0001139    57.32   0.000     .0063065     .006755
        545  |   .1989221   .0005473   363.45   0.000     .1978449    .1999993
        560  |  -.1819263   .0000937 -1941.16   0.000    -.1821107   -.1817418
        562  |  -.0001081   .0000937    -1.15   0.250    -.0002925    .0000764
        563  |   .0230487   .0000662   348.09   0.000     .0229184     .023179
        564  |    .076839   .0000979   784.48   0.000     .0766462    .0770317
        577  |  -.0480624   .0000452 -1064.23   0.000    -.0481513   -.0479736
        578  |   .0230487   .0000662   348.09   0.000     .0229184     .023179
        580  |   .3927712   .0003175  1236.91   0.000     .3921462    .3933962
        581  |   .0183543   .0000913   200.93   0.000     .0181745    .0185341
        583  |   .0126518   5.59e-06  2262.19   0.000     .0126408    .0126628
        584  |   .1850756   .0000701  2638.58   0.000     .1849376    .1852137
        588  |   .0766876   .0000913   839.51   0.000     .0765078    .0768674
        592  |   .0280335   .0001006   278.54   0.000     .0278354    .0282316
        593  |  -.1258861   .0001358  -926.69   0.000    -.1261535   -.1256187
        595  |  -.1057711   .0002479  -426.59   0.000    -.1062592   -.1052831
        598  |   .0808492    .000354   228.37   0.000     .0801524     .081546
        599  |  -.0854844   .0000359 -2382.07   0.000    -.0855551   -.0854138
        601  |   .1548842   .0002368   654.19   0.000     .1544182    .1553502
        604  |   .1184229   .0000758  1561.49   0.000     .1182736    .1185722
        607  |   .0079923   .0000701   113.94   0.000     .0078542    .0081303
        608  |   .0601667   .0000348  1728.97   0.000     .0600982    .0602352
        609  |   .0226216   .0001088   207.93   0.000     .0224074    .0228357
        611  |  -.0202514   .0010962   -18.47   0.000    -.0224089   -.0180939
        614  |  -.0478751   .0000783  -611.45   0.000    -.0480292    -.047721
        615  |  -.0455626   .0000937  -486.15   0.000    -.0457471   -.0453782
        616  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
        619  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
        620  |   -.064213   .0006449   -99.57   0.000    -.0654824   -.0629437
        623  |    .032146    .000129   249.10   0.000      .031892       .0324
        624  |  -.0730792   8.70e-07 -8.4e+04   0.000    -.0730809   -.0730775
        625  |  -.0426777   .0001435  -297.31   0.000    -.0429602   -.0423952
        626  |   .0151878   .0002421    62.72   0.000     .0147112    .0156644
        630  |    .226469   .0001762  1285.51   0.000     .2261222    .2268157
        631  |  -.2724378   .0002479 -1098.79   0.000    -.2729258   -.2719498
        635  |   .1027863     .00011   934.21   0.000     .1025697    .1030028
        636  |  -.1079576   .0006003  -179.84   0.000    -.1091391   -.1067761
        638  |  -.0304289   .0001448  -210.10   0.000     -.030714   -.0301438
        678  |  -.0123921   .0000717  -172.88   0.000    -.0125332    -.012251
        680  |  -.0393124   .0001078  -364.52   0.000    -.0395246   -.0391001
        681  |  -.2724378   .0002479 -1098.79   0.000    -.2729258   -.2719498
        683  |   .1699489   .0000605  2808.93   0.000     .1698299     .170068
        684  |   .0081558   .0003175    25.68   0.000     .0075309    .0087808
        686  |   .3284368   .0005872   559.29   0.000      .327281    .3295926
        687  |   .0602708   5.59e-06  1.1e+04   0.000     .0602598    .0602818
        689  |   .2275622   .0002479   917.80   0.000     .2270742    .2280502
        691  |   .1046511   .0001244   840.91   0.000     .1044061     .104896
        694  |  -.0778833   .0004791  -162.56   0.000    -.0788263   -.0769403
        697  |  -.0066457   .0000913   -72.75   0.000    -.0068255   -.0064659
        698  |   .0115445   .0000669   172.66   0.000     .0114129    .0116761
        700  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
        702  |   .0623531   .0008134    76.65   0.000     .0607521    .0639541
        704  |  -.1314548   .0004791  -274.37   0.000    -.1323977   -.1305118
        707  |   .0623531   .0008134    76.65   0.000     .0607521    .0639541
        710  |  -.0492438   .0006249   -78.80   0.000    -.0504738   -.0480138
        729  |  -.0298446   4.23e-06 -7051.57   0.000    -.0298529   -.0298363
        732  |   -.048758   .0008134   -59.94   0.000     -.050359    -.047157
        734  |  -.0021136   .0001095   -19.30   0.000    -.0023291    -.001898
        736  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
        738  |  -.0246242   .0006003   -41.02   0.000    -.0258057   -.0234428
        739  |  -.0268107   .0014485   -18.51   0.000    -.0296617   -.0239597
        740  |   .0173257   .0005872    29.50   0.000     .0161698    .0184815
        742  |  -.2724378   .0002479 -1098.79   0.000    -.2729258   -.2719498
        743  |   .1206563   6.52e-06  1.8e+04   0.000     .1206434    .1206691
        746  |   .0779917   .0001097   711.03   0.000     .0777758    .0782076
        747  |   .0603125   .0000217  2773.05   0.000     .0602697    .0603553
        748  |   .2629964   .0009265   283.85   0.000     .2611728      .26482
        749  |   .0594379   .0003175   187.18   0.000     .0588129    .0600629
        751  |  -.2750615   .0007699  -357.25   0.000    -.2765769   -.2735461
        753  |   .0168042   .0000461   364.45   0.000     .0167135     .016895
        755  |   -.174187   .0004306  -404.48   0.000    -.1750345   -.1733394
        758  |  -.1199372    .000274  -437.66   0.000    -.1204765   -.1193978
        759  |   .0600516   .0000794   755.91   0.000     .0598953     .060208
        761  |   .0030289   5.26e-06   575.97   0.000     .0030186    .0030393
        762  |  -.1334873   .0001592  -838.46   0.000    -.1338007   -.1331739
        765  |  -.0367157   .0001372  -267.52   0.000    -.0369858   -.0364456
        768  |   .0881874   .0000594  1483.42   0.000     .0880704    .0883044
        777  |   .3503758   .0006003   583.68   0.000     .3491943    .3515572
        778  |  -.1043135   .0008134  -128.24   0.000    -.1059145   -.1027125
        781  |   .0153822   .0001667    92.25   0.000      .015054    .0157103
        783  |   .0884158   .0008316   106.32   0.000      .086779    .0900526
        785  |   .0778499   .0001647   472.65   0.000     .0775257     .078174
        790  |  -.0683187   .0001435  -475.93   0.000    -.0686013   -.0680362
        791  |   -.042005   .0001174  -357.65   0.000    -.0422361   -.0417738
        831  |   .1266876   .0000913  1386.87   0.000     .1265078    .1268674
        832  |   .1427712   .0003175   449.61   0.000     .1421462    .1433962
        834  |  -.0224378   .0002479   -90.50   0.000    -.0229258   -.0219498
        837  |    .123701   .0001025  1207.00   0.000     .1234993    .1239027
        845  |   .0075735   .0000199   380.23   0.000     .0075343    .0076127
        846  |   .0623531   .0008134    76.65   0.000     .0607521    .0639541
        848  |  -.1079576   .0006003  -179.84   0.000    -.1091391   -.1067761
        849  |   .1510095   .0000605  2495.90   0.000     .1508905    .1511286
        850  |   .0601667   .0000348  1728.97   0.000     .0600982    .0602352
        851  |   .0787662   3.38e-06  2.3e+04   0.000     .0787595    .0787729
        853  |    .132411   .0001309  1011.72   0.000     .1321534    .1326686
        854  |   -.074187   .0004306  -172.27   0.000    -.0750345   -.0733394
        857  |   .2319351   .0019444   119.28   0.000     .2281081     .235762
        858  |  -.0239996   .0003579   -67.05   0.000     -.024704   -.0232951
        859  |   .2095669   .0000445  4704.19   0.000     .2094792    .2096545
        886  |  -.1205251   .0004022  -299.69   0.000    -.1213167   -.1197336
        887  |   .0565227   .0014485    39.02   0.000     .0536717    .0593736
        889  |   .0395156   .0000359  1101.12   0.000     .0394449    .0395862
        890  |  -.2768107   .0014485  -191.10   0.000    -.2796617   -.2739597
        892  |  -.0612756   .0009638   -63.58   0.000    -.0631726   -.0593786
        893  |   .0014821   .0007106     2.09   0.038     .0000834    .0028807
        895  |  -.0343242   .0000935  -367.18   0.000    -.0345082   -.0341402
        905  |    .056963   .0001189   479.03   0.000      .056729    .0571971
        908  |  -.0668429   .0001116  -598.78   0.000    -.0670626   -.0666232
        915  |   .0118189   .0003175    37.22   0.000     .0111939    .0124438
        918  |  -.2738954   .0003175  -862.55   0.000    -.2745204   -.2732704
        922  |  -.1302054   5.59e-06 -2.3e+04   0.000    -.1302164   -.1301944
        924  |   .2290198   .0008134   281.55   0.000     .2274188    .2306208
        925  |  -.0993486   .0000717 -1386.03   0.000    -.0994897   -.0992076
        927  |  -.0260247   .0000824  -315.69   0.000    -.0261869   -.0258624
        931  |  -.0924433   .0001237  -747.55   0.000    -.0926867   -.0921999
        934  |  -.1598691   .0008134  -196.54   0.000    -.1614701   -.1582681
        935  |   .1218991   .0004022   303.11   0.000     .1211076    .1226907
        936  |  -.0507015   .0000594  -852.86   0.000    -.0508185   -.0505845
        946  |   .1757386   .0000637  2760.99   0.000     .1756133    .1758639
        976  |   .0266876   .0000913   292.15   0.000     .0265078    .0268674
        977  |   .0200536   .0000538   372.54   0.000     .0199476    .0201595
        980  |   .0922426   .0002072   445.18   0.000     .0918348    .0926505
        981  |  -.0237132   .0002469   -96.06   0.000    -.0241991   -.0232274
        989  |  -.1235748   .0001931  -639.83   0.000    -.1239549   -.1231946
        992  |  -.1063958   5.59e-06 -1.9e+04   0.000    -.1064068   -.1063848
             |
       _cons |   .2680649   .0019444   137.86   0.000      .264238    .2718919
------------------------------------------------------------------------------

. estadd local Controls "No"

added macro:
           e(Controls) : "No"

. estadd local Controls1 " "

added macro:
          e(Controls1) : " "

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "Yes"

added macro:
          e(Controls5) : "Yes"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                         Number of obs = 20,064

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3072667   .0032572      .3008824    .3136511
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M4

. eststo M5:  reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool i.kunta19, clust
> er(kunta19)

Linear regression                               Number of obs     =     49,679
                                                F(14, 289)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0780
                                                Root MSE          =     .44689

                                (Std. err. adjusted for 290 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0092205   .0033733     2.73   0.007     .0025811    .0158598
     molincome |   .0039015   .0039417     0.99   0.323    -.0038566    .0116595
        female |   .1041828    .006279    16.59   0.000     .0918244    .1165412
           age |   .0063023   .0008293     7.60   0.000       .00467    .0079345
       foreign |  -.1337367   .0107048   -12.49   0.000     -.154806   -.1126673
               |
       moses1d |
            1  |   .1783803   .0198776     8.97   0.000      .139257    .2175036
            2  |   .0712333   .0150037     4.75   0.000     .0417029    .1007637
            3  |    .125564   .0104542    12.01   0.000      .104988      .14614
            4  |   .0467504    .007752     6.03   0.000     .0314929    .0620079
            5  |   .0089908   .0085387     1.05   0.293     -.007815    .0257967
            6  |   .0690563   .0111759     6.18   0.000     .0470598    .0910527
            7  |    .068759   .0122636     5.61   0.000     .0446217    .0928963
            9  |   .0107277   .0146753     0.73   0.465    -.0181563    .0396117
               |
     firstvote |   .1235445    .011109    11.12   0.000     .1016798    .1454093
1.mohighschool |   .1265887   .0055662    22.74   0.000     .1156334    .1375441
               |
       kunta19 |
            9  |   .2278681   .0017138   132.96   0.000     .2244951    .2312412
           10  |   .0310163    .001925    16.11   0.000     .0272274    .0348052
           16  |    .245928   .0044597    55.15   0.000     .2371505    .2547055
           18  |   .0352856   .0025963    13.59   0.000     .0301756    .0403957
           19  |  -.0154901   .0026802    -5.78   0.000    -.0207652   -.0102149
           20  |  -.1799173   .0037479   -48.00   0.000     -.187294   -.1725406
           43  |  -.1248412   .0124202   -10.05   0.000    -.1492867   -.1003957
           46  |   .1968936   .0069449    28.35   0.000     .1832247    .2105626
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          790  |  -.0743627   .0015848   -46.92   0.000     -.077482   -.0712434
          791  |  -.0097376   .0025678    -3.79   0.000    -.0147916   -.0046837
          831  |   .1139365   .0043233    26.35   0.000     .1054274    .1224455
          832  |   .1770827   .0036116    49.03   0.000     .1699744    .1841909
          834  |  -.0653759   .0046337   -14.11   0.000     -.074496   -.0562558
          837  |   .0862968   .0030882    27.94   0.000     .0802186    .0923751
          845  |  -.0165415   .0015379   -10.76   0.000    -.0195683   -.0135147
          846  |   .0884355   .0046379    19.07   0.000      .079307    .0975639
          848  |  -.0868947   .0023305   -37.29   0.000    -.0914816   -.0823079
          849  |   .1391652    .001566    88.87   0.000      .136083    .1422475
          850  |   .0321541   .0030255    10.63   0.000     .0261992    .0381089
          851  |    .025894   .0039003     6.64   0.000     .0182175    .0335705
          853  |   .0772952   .0038746    19.95   0.000     .0696692    .0849212
          854  |  -.0753753    .002635   -28.61   0.000    -.0805616    -.070189
          857  |    .327845   .0049401    66.36   0.000     .3181219    .3375681
          858  |  -.0291519   .0034287    -8.50   0.000    -.0359003   -.0224036
          859  |   .2097913   .0030625    68.50   0.000     .2037636     .215819
          886  |  -.1047752   .0020705   -50.60   0.000    -.1088504      -.1007
          887  |  -.0352296   .0083203    -4.23   0.000    -.0516056   -.0188536
          889  |   .0590511   .0014803    39.89   0.000     .0561376    .0619646
          890  |    -.27956   .0060146   -46.48   0.000     -.291398    -.267722
          892  |  -.0445941   .0030914   -14.42   0.000    -.0506787   -.0385095
          893  |   -.042986   .0025827   -16.64   0.000    -.0480692   -.0379027
          895  |  -.0187591   .0015884   -11.81   0.000    -.0218855   -.0156327
          905  |   .0348834   .0021227    16.43   0.000     .0307054    .0390614
          908  |   -.062797   .0015788   -39.77   0.000    -.0659045   -.0596895
          915  |   .0023253   .0028411     0.82   0.414    -.0032666    .0079173
          918  |  -.3297827   .0088176   -37.40   0.000    -.3471374   -.3124279
          922  |  -.1279539   .0025564   -50.05   0.000    -.1329854   -.1229225
          924  |   .2190879   .0062286    35.17   0.000     .2068287    .2313471
          925  |  -.1098916   .0035645   -30.83   0.000    -.1169072    -.102876
          927  |  -.0373133   .0014233   -26.22   0.000    -.0401148   -.0345119
          931  |  -.0673719   .0015685   -42.95   0.000    -.0704591   -.0642847
          934  |  -.1855413   .0024831   -74.72   0.000    -.1904285   -.1806541
          935  |   .1188732   .0012602    94.33   0.000     .1163928    .1213535
          936  |  -.0640555   .0031278   -20.48   0.000    -.0702117   -.0578994
          946  |   .1783446   .0010272   173.62   0.000     .1763228    .1803664
          976  |  -.0510969   .0039742   -12.86   0.000    -.0589189   -.0432749
          977  |   .0217583   .0017938    12.13   0.000     .0182277    .0252888
          980  |   .0963893   .0017044    56.55   0.000     .0930346    .0997439
          981  |   -.022763   .0023897    -9.53   0.000    -.0274664   -.0180596
          989  |  -.1170691   .0011795   -99.25   0.000    -.1193905   -.1147476
          992  |  -.1190996   .0027939   -42.63   0.000    -.1245987   -.1136006
               |
         _cons |   -.041343   .0383229    -1.08   0.282    -.1167704    .0340843
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "Yes"

added macro:
          e(Controls5) : "Yes"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                         Number of obs = 19,879

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3083656   .0032756      .3019453     .314786
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M5

. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local numbers "& (1) & (2) & (3) & (4) & (5) \\ \hline"

. local emptyrow "& & & & &  \\ "

. local line "& & & & & \hline \\ "

. 
. local dstars " "

. local sestars " "

. esttab, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("Controls Controls" "Controls1 \phantom{controls}"  "Controls2 \phantom{controls}" "Controls3 \phantom{c
> ontrols}" "Controls4 \phantom{controls}"  "Controls5 Municipality FE" "umean Untreated $\bar{Y}$" "N Observation
> s") ///
> sfmt(%9.3f %9.3f %9.3f %9.3f  %9.3f  %9.3f  %9.3f %9.0fc) mlabels(none) nonumbers posthead("`header'" "`emptyrow
> '" "`numbers'" "`emptyrow'") ///
> refcat(treated "", nolabel below) title("Average Treatment Effect") nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

Average Treatment Effect
----------------------------------------------------------------------------------------------------
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & & &  \\ 
& (1) & (2) & (3) & (4) & (5) \\ \hline
& & & & &  \\ 
Treatments Pooled           0.009***        0.009**         0.009***        0.009**         0.009***
                          (0.003)         (0.003)         (0.003)         (0.003)         (0.003)   

                                                                                                    
----------------------------------------------------------------------------------------------------
Controls                       No    Female, Age, Immigrant,    Female, Age, Immigrant,              No    Female,
>  Age, Immigrant,   
\phantom{controls}                      Ln Income      Ln Income,                      Ln Income,   
\phantom{controls}                                   SES Background,                    SES Background,   
\phantom{controls}                                   Educational Background,                    Educational Backgr
> ound,   
\phantom{controls}                                   First Time Eligble to Vote                    First Time Elig
> ble to Vote   
Municipality FE                No              No              No             Yes             Yes   
Untreated $\bar{Y}$         0.307           0.308           0.308           0.307           0.308   
Observations               50,140          49,679          49,679          50,140          49,679   
----------------------------------------------------------------------------------------------------

. 
. 
. 
. *************************************************
. *****TABLE A1
. *************************************************
. 
. 
. eststo clear

. eststo M1: logit voted22 treated, cluster(kunta19)

Iteration 0:   log pseudolikelihood = -31140.679  
Iteration 1:   log pseudolikelihood = -31138.557  
Iteration 2:   log pseudolikelihood = -31138.557  

Logistic regression                                     Number of obs = 50,140
                                                        Wald chi2(1)  =   7.12
                                                        Prob > chi2   = 0.0076
Log pseudolikelihood = -31138.557                       Pseudo R2     = 0.0001

                              (Std. err. adjusted for 290 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     treated |   .0405568   .0152029     2.67   0.008     .0107595     .070354
       _cons |  -.8129288   .0616881   -13.18   0.000    -.9338353   -.6920222
------------------------------------------------------------------------------

. estadd local Controls "No"

added macro:
           e(Controls) : "No"

. estadd local Controls1 " "

added macro:
          e(Controls1) : " "

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                         Number of obs = 20,064

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3072667   .0032572      .3008824    .3136511
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M1

. eststo M2: logit voted22 treated molincome female age foreign, cluster(kunta19)

Iteration 0:   log pseudolikelihood = -30900.768  
Iteration 1:   log pseudolikelihood = -30286.119  
Iteration 2:   log pseudolikelihood = -30270.669  
Iteration 3:   log pseudolikelihood = -30270.612  
Iteration 4:   log pseudolikelihood = -30270.612  

Logistic regression                                    Number of obs =  49,679
                                                       Wald chi2(5)  = 1422.73
                                                       Prob > chi2   =  0.0000
Log pseudolikelihood = -30270.612                      Pseudo R2     =  0.0204

                              (Std. err. adjusted for 290 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     treated |   .0415738   .0160781     2.59   0.010     .0100613    .0730863
   molincome |    .100691   .0196522     5.12   0.000     .0621733    .1392086
      female |   .5311916   .0260643    20.38   0.000     .4801065    .5822768
         age |   .0172842   .0064188     2.69   0.007     .0047036    .0298648
     foreign |  -1.286763   .0851168   -15.12   0.000    -1.453589   -1.119937
       _cons |  -2.392115   .2081541   -11.49   0.000     -2.80009   -1.984141
------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income"

added macro:
          e(Controls1) : "Ln Income"

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                         Number of obs = 19,879

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3083656   .0032756      .3019453     .314786
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M2

. eststo M3:  logit voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(kunta
> 19)

Iteration 0:   log pseudolikelihood = -30900.768  
Iteration 1:   log pseudolikelihood = -29336.439  
Iteration 2:   log pseudolikelihood = -29307.294  
Iteration 3:   log pseudolikelihood = -29307.154  
Iteration 4:   log pseudolikelihood = -29307.154  

Logistic regression                                    Number of obs =  49,679
                                                       Wald chi2(15) = 3655.99
                                                       Prob > chi2   =  0.0000
Log pseudolikelihood = -29307.154                      Pseudo R2     =  0.0516

                                (Std. err. adjusted for 290 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0445209   .0162025     2.75   0.006     .0127645    .0762772
     molincome |   .0145393   .0192782     0.75   0.451    -.0232452    .0523239
        female |   .5033129    .026314    19.13   0.000     .4517385    .5548873
           age |   .0372696   .0045025     8.28   0.000     .0284448    .0460943
       foreign |  -1.017054   .0835031   -12.18   0.000    -1.180717   -.8533907
               |
       moses1d |
            1  |   .9038082   .1092241     8.27   0.000     .6897328    1.117884
            2  |   .3841584   .0755959     5.08   0.000     .2359931    .5323237
            3  |   .6114884   .0564894    10.82   0.000     .5007712    .7222056
            4  |   .2529757   .0458486     5.52   0.000      .163114    .3428374
            5  |   .0297779   .0566608     0.53   0.599    -.0812753     .140831
            6  |   .3787817   .0707981     5.35   0.000       .24002    .5175433
            7  |   .3779066   .0753535     5.02   0.000     .2302164    .5255967
            9  |   .0371253   .0809721     0.46   0.647    -.1215771    .1958277
               |
     firstvote |   .5773617    .051196    11.28   0.000     .4770194     .677704
1.mohighschool |   .6248319   .0314217    19.89   0.000     .5632465    .6864172
         _cons |  -2.565988   .2135073   -12.02   0.000    -2.984454   -2.147521
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                         Number of obs = 19,879

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3083656   .0032756      .3019453     .314786
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M3

. eststo M4:  logit voted22 treated i.kunta19, cluster(kunta19)

note: 43.kunta19 != 0 predicts failure perfectly;
      43.kunta19 omitted and 1 obs not used.

note: 47.kunta19 != 0 predicts failure perfectly;
      47.kunta19 omitted and 2 obs not used.

note: 60.kunta19 != 0 predicts failure perfectly;
      60.kunta19 omitted and 1 obs not used.

note: 81.kunta19 != 0 predicts failure perfectly;
      81.kunta19 omitted and 1 obs not used.

note: 90.kunta19 != 0 predicts failure perfectly;
      90.kunta19 omitted and 6 obs not used.

note: 105.kunta19 != 0 predicts failure perfectly;
      105.kunta19 omitted and 4 obs not used.

note: 142.kunta19 != 0 predicts failure perfectly;
      142.kunta19 omitted and 1 obs not used.

note: 170.kunta19 != 0 predicts success perfectly;
      170.kunta19 omitted and 1 obs not used.

note: 181.kunta19 != 0 predicts failure perfectly;
      181.kunta19 omitted and 4 obs not used.

note: 213.kunta19 != 0 predicts failure perfectly;
      213.kunta19 omitted and 3 obs not used.

note: 218.kunta19 != 0 predicts failure perfectly;
      218.kunta19 omitted and 4 obs not used.

note: 256.kunta19 != 0 predicts failure perfectly;
      256.kunta19 omitted and 2 obs not used.

note: 284.kunta19 != 0 predicts failure perfectly;
      284.kunta19 omitted and 2 obs not used.

note: 312.kunta19 != 0 predicts failure perfectly;
      312.kunta19 omitted and 7 obs not used.

note: 316.kunta19 != 0 predicts failure perfectly;
      316.kunta19 omitted and 2 obs not used.

note: 320.kunta19 != 0 predicts failure perfectly;
      320.kunta19 omitted and 10 obs not used.

note: 407.kunta19 != 0 predicts failure perfectly;
      407.kunta19 omitted and 2 obs not used.

note: 416.kunta19 != 0 predicts failure perfectly;
      416.kunta19 omitted and 1 obs not used.

note: 480.kunta19 != 0 predicts failure perfectly;
      480.kunta19 omitted and 1 obs not used.

note: 538.kunta19 != 0 predicts failure perfectly;
      538.kunta19 omitted and 2 obs not used.

note: 616.kunta19 != 0 predicts failure perfectly;
      616.kunta19 omitted and 2 obs not used.

note: 619.kunta19 != 0 predicts failure perfectly;
      619.kunta19 omitted and 1 obs not used.

note: 631.kunta19 != 0 predicts failure perfectly;
      631.kunta19 omitted and 2 obs not used.

note: 681.kunta19 != 0 predicts failure perfectly;
      681.kunta19 omitted and 2 obs not used.

note: 700.kunta19 != 0 predicts failure perfectly;
      700.kunta19 omitted and 1 obs not used.

note: 736.kunta19 != 0 predicts failure perfectly;
      736.kunta19 omitted and 1 obs not used.

note: 742.kunta19 != 0 predicts failure perfectly;
      742.kunta19 omitted and 2 obs not used.

note: 751.kunta19 != 0 predicts failure perfectly;
      751.kunta19 omitted and 5 obs not used.

note: 890.kunta19 != 0 predicts failure perfectly;
      890.kunta19 omitted and 1 obs not used.

note: 918.kunta19 != 0 predicts failure perfectly;
      918.kunta19 omitted and 3 obs not used.

Iteration 0:   log pseudolikelihood = -31111.016  
Iteration 1:   log pseudolikelihood = -30585.472  
Iteration 2:   log pseudolikelihood = -30579.167  
Iteration 3:   log pseudolikelihood = -30579.102  
Iteration 4:   log pseudolikelihood = -30579.102  

Logistic regression                                     Number of obs = 50,063
                                                        Wald chi2(4)  =      .
                                                        Prob > chi2   =      .
Log pseudolikelihood = -30579.102                       Pseudo R2     = 0.0171

                              (Std. err. adjusted for 260 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     treated |   .0416328   .0160123     2.60   0.009     .0102492    .0730163
             |
     kunta19 |
          9  |   .9774351   .0005909  1654.23   0.000      .976277    .9785932
         10  |   .1991475   .0005358   371.66   0.000     .1980972    .2001977
         16  |   1.003022   .0092504   108.43   0.000     .9848918    1.021153
         18  |   .2751406   .0041245    66.71   0.000     .2670568    .2832244
         19  |    .082931   .0008873    93.47   0.000      .081192    .0846701
         20  |  -.9123565   .0021673  -420.96   0.000    -.9166043   -.9081087
         43  |          0  (empty)
         46  |   1.003022   .0092504   108.43   0.000     .9848918    1.021153
         47  |          0  (empty)
         49  |   .2673355   .0009337   286.31   0.000     .2655054    .2691656
         50  |   .6915195    .000077  8985.41   0.000     .6913686    .6916703
         51  |    .190296    .000738   257.84   0.000     .1888495    .1917425
         52  |   -.126896   .0028215   -44.97   0.000     -.132426    -.121366
         60  |          0  (empty)
         61  |  -.2116525   .0004424  -478.46   0.000    -.2125195   -.2107855
         69  |   .2275054   .0004748   479.17   0.000     .2265748    .2284359
         71  |   .3765624   .0001124  3350.04   0.000     .3763421    .3767827
         72  |   .1695915   .0005646   300.38   0.000     .1684849     .170698
         74  |    .539421   .0008599   627.29   0.000     .5377355    .5411064
         75  |  -.2298727   .0019116  -120.25   0.000    -.2336194   -.2261261
         77  |   -.035317   .0010693   -33.03   0.000    -.0374128   -.0332212
         78  |  -.4000501   .0027495  -145.50   0.000    -.4054389   -.3946612
         79  |  -.4194524   .0003674 -1141.70   0.000    -.4201725   -.4187323
         81  |          0  (empty)
         82  |  -.6342991   .0015231  -416.47   0.000    -.6372842    -.631314
         86  |   .0271189   .0006733    40.28   0.000     .0257993    .0284385
         90  |          0  (empty)
         91  |   .4438909   .0004859   913.49   0.000     .4429384    .4448433
         92  |  -.1604498    .000329  -487.65   0.000    -.1610947    -.159805
         97  |   .9822058   .0012442   789.40   0.000     .9797672    .9846445
         98  |    .420639   .0004712   892.67   0.000     .4197154    .4215625
         99  |  -.1060794    .005185   -20.46   0.000    -.1162418   -.0959169
        102  |  -.0006135   .0004412    -1.39   0.164    -.0014783    .0002513
        103  |   .9613895   .0067619   142.18   0.000     .9481364    .9746425
        105  |          0  (empty)
        106  |  -.0255354   .0011667   -21.89   0.000    -.0278222   -.0232487
        108  |   .0420314   .0002069   203.17   0.000      .041626    .0424369
        109  |   .1585402   .0001796   882.93   0.000     .1581883    .1588921
        111  |  -.2234842   .0005392  -414.50   0.000    -.2245409   -.2224275
        139  |   .3316101   .0003074  1078.83   0.000     .3310076    .3322125
        140  |  -.7631283   .0002769 -2755.68   0.000    -.7636711   -.7625855
        142  |          0  (empty)
        143  |   .8680842   .0026479   327.84   0.000     .8628944     .873274
        145  |   .0628503   .0000304  2065.16   0.000     .0627906    .0629099
        146  |  -.3508238   .0011687  -300.18   0.000    -.3531144   -.3485331
        148  |  -1.661188   .0004953 -3353.70   0.000    -1.662159   -1.660217
        149  |   .9613895   .0067619   142.18   0.000     .9481364    .9746425
        151  |   .4017883   .0007215   556.87   0.000     .4003742    .4032025
        152  |   -.824498   .0045324  -181.91   0.000    -.8333814   -.8156146
        153  |   .5278601   .0003179  1660.29   0.000      .527237    .5284832
        165  |  -.1648152   .0004135  -398.56   0.000    -.1656257   -.1640047
        167  |   .1856439   .0004976   373.10   0.000     .1846687    .1866191
        169  |   .0614799   .0004958   123.99   0.000     .0605081    .0624517
        170  |          0  (empty)
        171  |   .2959336   .0038639    76.59   0.000     .2883604    .3035067
        172  |  -1.099715   .0002268 -4848.52   0.000    -1.100159    -1.09927
        176  |  -.4083766   .0004529  -901.59   0.000    -.4092643   -.4074888
        177  |  -.8526842   .0002967 -2873.51   0.000    -.8532658   -.8521026
        178  |   .5642227   .0035814   157.54   0.000     .5572033     .571242
        179  |   .3487966   .0003338  1045.01   0.000     .3481425    .3494508
        181  |          0  (empty)
        182  |  -.3342006    .000425  -786.44   0.000    -.3350335   -.3333677
        186  |  -.4100369   .0010896  -376.32   0.000    -.4121724   -.4079013
        202  |   .1446677    .000442   327.28   0.000     .1438013     .145534
        204  |   .4557438   .0047791    95.36   0.000     .4463771    .4651106
        205  |   .0048127   .0008667     5.55   0.000      .003114    .0065115
        208  |    .286014   .0000459  6227.33   0.000      .285924     .286104
        211  |  -.0025035   .0002848    -8.79   0.000    -.0030617   -.0019454
        213  |          0  (empty)
        214  |   .1695915   .0005646   300.38   0.000     .1684849     .170698
        216  |  -.1820495   .0007759  -234.63   0.000    -.1835702   -.1805288
        217  |   .2354949   .0006199   379.86   0.000     .2342798    .2367099
        218  |          0  (empty)
        224  |   .0883808   .0007831   112.86   0.000      .086846    .0899156
        226  |   .1357917   .0006515   208.44   0.000     .1345148    .1370685
        230  |   .6218201   .0001974  3150.58   0.000     .6214333     .622207
        231  |  -.4083766   .0004529  -901.59   0.000    -.4092643   -.4074888
        232  |  -.2729889   .0002632 -1037.13   0.000    -.2735048   -.2724731
        233  |  -.0464203   .0015869   -29.25   0.000    -.0495306     -.04331
        235  |   .2820554   .0014742   191.33   0.000      .279166    .2849447
        236  |     .34009    .000526   646.58   0.000     .3390591    .3411209
        239  |   .6218201   .0001974  3150.58   0.000     .6214333     .622207
        240  |  -.0956034   .0004615  -207.16   0.000    -.0965079   -.0946989
        241  |  -1.909376     .00068 -2807.96   0.000    -1.910708   -1.908043
        244  |   .3748307   .0004041   927.64   0.000     .3740388    .3756227
        245  |  -.0867518   .0001987  -436.55   0.000    -.0871413   -.0863623
        249  |  -1.031638   .0015881  -649.60   0.000    -1.034751   -1.028525
        250  |   -.126896   .0028215   -44.97   0.000     -.132426    -.121366
        256  |          0  (empty)
        257  |   .0087814    .000744    11.80   0.000     .0073232    .0102395
        260  |  -.8883668   .0016563  -536.37   0.000     -.891613   -.8851206
        261  |   .1348213   .0011776   114.49   0.000     .1325133    .1371293
        263  |  -.0336555   .0004318   -77.95   0.000    -.0345017   -.0328093
        265  |   .0588936   .0014882    39.57   0.000     .0559768    .0618104
        271  |  -.1217122   .0008355  -145.67   0.000    -.1233498   -.1200746
        272  |   .0255684   .0001505   169.87   0.000     .0252733    .0258634
        273  |  -.4083766   .0004529  -901.59   0.000    -.4092643   -.4074888
        275  |  -.6411897   .0041547  -154.33   0.000    -.6493328   -.6330467
        276  |    .187319   .0001945   962.88   0.000     .1869378    .1877003
        280  |   2.372788   .0029414   806.67   0.000     2.367023    2.378553
        284  |          0  (empty)
        285  |  -.0481017   .0021177   -22.71   0.000    -.0522524    -.043951
        286  |   .1292763   .0009529   135.67   0.000     .1274087    .1311439
        287  |   .7895804   .0005223  1511.66   0.000     .7885566    .7906041
        288  |   .5725356   .0003893  1470.77   0.000     .5717726    .5732986
        290  |   1.668478   .0013754  1213.06   0.000     1.665782    1.671174
        291  |   1.668478   .0013754  1213.06   0.000     1.665782    1.671174
        297  |   .5763941   .0000833  6917.15   0.000     .5762308    .5765574
        300  |    .174825   .0007221   242.11   0.000     .1734098    .1762403
        301  |  -.1286209   .0034811   -36.95   0.000    -.1354439    -.121798
        304  |   .2820554   .0014742   191.33   0.000      .279166    .2849447
        305  |  -.5608587   .0008717  -643.42   0.000    -.5625672   -.5591503
        309  |  -.5637925   .0009418  -598.63   0.000    -.5656384   -.5619466
        312  |          0  (empty)
        316  |          0  (empty)
        317  |  -.2776029   .0015068  -184.23   0.000    -.2805562   -.2746497
        320  |          0  (empty)
        322  |  -.2686598   .0002748  -977.79   0.000    -.2691983   -.2681213
        398  |  -.0963405   .0005742  -167.77   0.000     -.097466    -.095215
        399  |  -1.219348   .0004847 -2515.87   0.000    -1.220298   -1.218398
        400  |  -.4790541   .0031481  -152.17   0.000    -.4852242    -.472884
        402  |  -.4006347   .0002328 -1721.29   0.000    -.4010909   -.4001785
        403  |   .5716112   .0007445   767.78   0.000      .570152    .5730704
        405  |   .6671829   .0001479  4510.50   0.000      .666893    .6674729
        407  |          0  (empty)
        408  |  -.2416878   .0011533  -209.55   0.000    -.2439483   -.2394273
        410  |  -.0852775   .0001379  -618.42   0.000    -.0855478   -.0850073
        416  |          0  (empty)
        418  |    .301655   .0014039   214.87   0.000     .2989034    .3044066
        420  |   .5808627   .0028138   206.44   0.000     .5753478    .5863776
        422  |   -.317339   .0006537  -485.42   0.000    -.3186203   -.3160577
        423  |   .7773508   .0003733  2082.34   0.000     .7766191    .7780825
        425  |   .4487005   .0001561  2874.82   0.000     .4483946    .4490064
        426  |  -.1333179   .0007481  -178.21   0.000    -.1347841   -.1318516
        430  |   .0658223   .0011728    56.12   0.000     .0635236    .0681209
        433  |  -.6273765   .0011331  -553.66   0.000    -.6295974   -.6251556
        434  |  -.4083766   .0004529  -901.59   0.000    -.4092643   -.4074888
        436  |   .6454976   .0012555   514.14   0.000     .6430368    .6479583
        440  |   .5559244   .0067619    82.21   0.000     .5426713    .5691774
        441  |   .8061742    .000371  2172.84   0.000      .805447    .8069014
        444  |  -.2378443    .000436  -545.57   0.000    -.2386988   -.2369899
        445  |   .3928207   .0004907   800.57   0.000      .391859    .3937824
        475  |   .8162939   .0000675  1.2e+04   0.000     .8161617    .8164262
        480  |          0  (empty)
        481  |   .1380773   .0004713   292.96   0.000     .1371535     .139001
        483  |   .9926146    .005248   189.14   0.000     .9823288      1.0029
        484  |  -.8007589    .004578  -174.92   0.000    -.8097316   -.7917863
        489  |   1.003022   .0092504   108.43   0.000     .9848918    1.021153
        491  |   .0586112   .0015965    36.71   0.000     .0554822    .0617402
        494  |   .5552077   .0001344  4131.07   0.000     .5549443    .5554712
        495  |   .9613895   .0067619   142.18   0.000     .9481364    .9746425
        498  |   .6915195    .000077  8985.41   0.000     .6913686    .6916703
        499  |  -.1545725   .0005101  -303.05   0.000    -.1555721   -.1535728
        500  |   .3222838   .0002491  1293.55   0.000     .3217955    .3227721
        503  |  -.3978566   .0002725 -1460.00   0.000    -.3983907   -.3973225
        505  |   .1348213   .0011776   114.49   0.000     .1325133    .1371293
        507  |   -.051238   .0002649  -193.39   0.000    -.0517573   -.0507188
        508  |  -.1637562   .0008821  -185.65   0.000     -.165485   -.1620274
        529  |  -.2202817   .0017709  -124.39   0.000    -.2237526   -.2168107
        531  |  -.4574095   .0005473  -835.78   0.000    -.4584821   -.4563368
        535  |   .5415281    .000509  1063.85   0.000     .5405304    .5425257
        536  |   -.405819    .000529  -767.15   0.000    -.4068558   -.4047822
        538  |          0  (empty)
        541  |  -.4028302   .0016784  -240.00   0.000    -.4061198   -.3995405
        543  |   .0327232   .0005338    61.31   0.000     .0316771    .0337694
        545  |   .8680842   .0026479   327.84   0.000     .8628944     .873274
        560  |  -1.322447   .0003814 -3467.77   0.000    -1.323195     -1.3217
        562  |  -.0006135   .0004412    -1.39   0.164    -.0014783    .0002513
        563  |   .1132681   .0003041   372.47   0.000     .1126721    .1138642
        564  |   .3595998   .0004362   824.30   0.000     .3587448    .3604549
        577  |  -.2575184   .0002279 -1129.84   0.000    -.2579652   -.2570717
        578  |   .1132681   .0003041   372.47   0.000     .1126721    .1138642
        580  |   1.668478   .0013754  1213.06   0.000     1.665782    1.671174
        581  |   .0906526   .0004238   213.90   0.000     .0898219    .0914832
        583  |   .0628503   .0000304  2065.16   0.000     .0627906    .0629099
        584  |    .811231   .0002706  2997.75   0.000     .8107006    .8117614
        588  |   .3589408   .0004052   885.78   0.000     .3581465     .359735
        592  |   .1370627   .0004644   295.11   0.000     .1361524     .137973
        593  |  -.7766673   .0006783 -1145.09   0.000    -.7779967    -.775338
        595  |  -.6273765   .0011331  -553.66   0.000    -.6295974   -.6251556
        598  |   .3769271   .0016393   229.93   0.000      .373714    .3801401
        599  |  -.4868665    .000141 -3453.37   0.000    -.4871429   -.4865902
        601  |   .6885474   .0010655   646.21   0.000     .6864591    .6906358
        604  |   .5382145    .000396  1359.06   0.000     .5374384    .5389907
        607  |   .0399416   .0003274   122.00   0.000     .0392999    .0405832
        608  |   .2855189   .0001443  1978.51   0.000      .285236    .2858017
        609  |   .1112237   .0005045   220.47   0.000     .1102349    .1122125
        611  |  -.1060794    .005185   -20.46   0.000    -.1162418   -.0959169
        614  |  -.2567564   .0003529  -727.62   0.000     -.257448   -.2560648
        615  |  -.2435792   .0004262  -571.45   0.000    -.2444146   -.2427438
        616  |          0  (empty)
        619  |          0  (empty)
        620  |  -.3505967   .0030393  -115.36   0.000    -.3565535   -.3446398
        623  |   .1565192   .0005967   262.30   0.000     .1553496    .1576887
        624  |  -.4072688   .0000278 -1.5e+04   0.000    -.4073233   -.4072143
        625  |  -.2266812   .0006883  -329.32   0.000    -.2280303   -.2253321
        626  |   .0753216   .0011333    66.46   0.000     .0731003     .077543
        630  |   .9770014   .0007577  1289.44   0.000     .9755163    .9784865
        631  |          0  (empty)
        635  |   .4718262   .0004849   973.13   0.000     .4708759    .4727765
        636  |  -.6377484   .0028419  -224.41   0.000    -.6433185   -.6321783
        638  |  -.1589049   .0006905  -230.12   0.000    -.1602584   -.1575515
        678  |    -.06327   .0003411  -185.47   0.000    -.0639386   -.0626014
        680  |  -.2078938   .0005197  -400.06   0.000    -.2089123   -.2068753
        681  |          0  (empty)
        683  |   .7501383     .00023  3261.15   0.000     .7496875    .7505892
        684  |   .0408737   .0014893    27.45   0.000     .0379548    .0437926
        686  |   1.391876   .0028778   483.66   0.000     1.386236    1.397516
        687  |    .286014   .0000459  6227.33   0.000      .285924     .286104
        689  |   .9822058   .0012442   789.40   0.000     .9797672    .9846445
        691  |   .4797474   .0005522   868.81   0.000     .4786651    .4808297
        694  |    -.43559   .0022695  -191.93   0.000    -.4400381   -.4311419
        697  |  -.0336555   .0004318   -77.95   0.000    -.0345017   -.0328093
        698  |   .0574474   .0003108   184.82   0.000     .0568382    .0580567
        700  |          0  (empty)
        702  |   .2959336   .0038639    76.59   0.000     .2883604    .3035067
        704  |   -.818601   .0022838  -358.44   0.000    -.8230772   -.8141248
        707  |   .2959336   .0038639    76.59   0.000     .2883604    .3035067
        710  |  -.2660457   .0029354   -90.63   0.000    -.2717989   -.2602924
        729  |  -.1559773   .0000296 -5269.57   0.000    -.1560353   -.1559192
        732  |  -.2637253   .0038307   -68.85   0.000    -.2712333   -.2562172
        734  |  -.0105871   .0005157   -20.53   0.000    -.0115979   -.0095763
        736  |          0  (empty)
        738  |   -.126896   .0028215   -44.97   0.000     -.132426    -.121366
        739  |  -.1372228   .0067619   -20.29   0.000    -.1504759   -.1239697
        740  |   .0854949   .0027781    30.77   0.000       .08005    .0909398
        742  |          0  (empty)
        743  |   .5474619   8.49e-06  6.5e+04   0.000     .5474453    .5474786
        746  |   .3646513   .0004911   742.46   0.000     .3636887    .3656139
        747  |   .2862121    .000122  2345.19   0.000     .2859729    .2864513
        748  |   1.121953   .0042924   261.38   0.000      1.11354    1.130366
        749  |   .2820554   .0014742   191.33   0.000      .279166    .2849447
        751  |          0  (empty)
        753  |   .0831252   .0002115   392.99   0.000     .0827107    .0835398
        755  |   -1.22349   .0020697  -591.15   0.000    -1.227547   -1.219434
        758  |  -.7290829   .0013234  -550.91   0.000    -.7316768    -.726489
        759  |   .2849718   .0003545   803.87   0.000     .2842769    .2856666
        761  |   .0152109   .0000238   640.10   0.000     .0151643    .0152575
        762  |  -.8382269   .0007901 -1060.93   0.000    -.8397754   -.8366783
        765  |  -.1934036   .0006569  -294.43   0.000     -.194691   -.1921161
        768  |   .4092885   .0003088  1325.40   0.000     .4086832    .4098937
        777  |   1.482663   .0027281   543.48   0.000     1.477316     1.48801
        778  |  -.6204218    .003814  -162.67   0.000    -.6278972   -.6129464
        781  |   .0762447   .0007792    97.85   0.000     .0747176    .0777718
        783  |   .4093077   .0038781   105.54   0.000     .4017067    .4169087
        785  |   .3639771   .0007501   485.24   0.000     .3625069    .3654472
        790  |  -.3772646   .0006964  -541.77   0.000    -.3786294   -.3758997
        791  |  -.2234842   .0005392  -414.50   0.000    -.2245409   -.2224275
        831  |   .5725356   .0003893  1470.77   0.000     .5717726    .5732986
        832  |   .6387623   .0014496   440.64   0.000     .6359211    .6416035
        834  |  -.1165148   .0011609  -100.36   0.000    -.1187901   -.1142394
        837  |   .5600578   .0004427  1265.08   0.000     .5591902    .5609255
        845  |   .0378429   .0000913   414.63   0.000     .0376641    .0380218
        846  |   .2959336   .0038639    76.59   0.000     .2883604    .3035067
        848  |  -.6377484   .0028419  -224.41   0.000    -.6433185   -.6321783
        849  |   .6730074   .0002361  2850.30   0.000     .6725446    .6734702
        850  |   .2855189   .0001443  1978.51   0.000      .285236    .2858017
        851  |   .3681499   9.67e-06  3.8e+04   0.000      .368131    .3681689
        853  |   .5962676   .0005737  1039.36   0.000     .5951432     .597392
        854  |  -.4125242   .0020422  -202.00   0.000    -.4165268   -.4085217
        857  |   1.003022   .0092504   108.43   0.000     .9848918    1.021153
        858  |  -.1239355   .0016879   -73.42   0.000    -.1272438   -.1206272
        859  |   .9096164    .000142  6406.27   0.000     .9093382    .9098947
        886  |  -.7388182   .0018568  -397.90   0.000    -.7424575   -.7351789
        887  |   .2682423   .0067619    39.67   0.000     .2549892    .2814954
        889  |   .1910664   .0001818  1050.78   0.000       .19071    .1914227
        890  |          0  (empty)
        892  |  -.3320066   .0045211   -73.44   0.000    -.3408678   -.3231455
        893  |   .0069628   .0033578     2.07   0.038     .0003816    .0135439
        895  |  -.1802966   .0004506  -400.14   0.000    -.1811797   -.1794134
        905  |   .2710156   .0005412   500.76   0.000     .2699549    .2720764
        908  |  -.3683482   .0005461  -674.48   0.000    -.3694186   -.3672779
        915  |   .0588936   .0014882    39.57   0.000     .0559768    .0618104
        918  |          0  (empty)
        922  |  -.8126789    .000016 -5.1e+04   0.000    -.8127102   -.8126476
        924  |   .9891451   .0039135   252.75   0.000     .9814748    .9968154
        925  |  -.5799969   .0003689 -1572.23   0.000    -.5807199   -.5792739
        927  |  -.1352074   .0003961  -341.38   0.000    -.1359836   -.1344311
        931  |  -.5323938   .0006106  -871.86   0.000    -.5335906    -.531197
        934  |  -1.090447   .0037973  -287.16   0.000     -1.09789   -1.083004
        935  |   .5532699   .0019372   285.61   0.000     .5494731    .5570666
        936  |  -.2729889   .0002632 -1037.13   0.000    -.2735048   -.2724731
        946  |   .7735664    .000243  3183.39   0.000     .7730901    .7740427
        976  |   .1306614   .0004212   310.25   0.000     .1298359    .1314868
        977  |   .0988573   .0002468   400.56   0.000     .0983736     .099341
        980  |   .4265204   .0009456   451.07   0.000     .4246671    .4283737
        981  |  -.1225771   .0011672  -105.01   0.000    -.1248648   -.1202894
        989  |  -.7579487   .0009459  -801.28   0.000    -.7598027   -.7560947
        992  |  -.6303473   8.24e-06 -7.6e+04   0.000    -.6303634   -.6303311
             |
       _cons |  -1.003022   .0092504  -108.43   0.000    -1.021153   -.9848918
------------------------------------------------------------------------------

. estadd local Controls "No"

added macro:
           e(Controls) : "No"

. estadd local Controls1 " "

added macro:
          e(Controls1) : " "

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "Yes"

added macro:
          e(Controls5) : "Yes"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                         Number of obs = 20,045

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |    .307558   .0032596      .3011689    .3139471
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M4

. eststo M5:  logit voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool i.kunta19, clu
> ster(kunta19)

note: 43.kunta19 != 0 predicts failure perfectly;
      43.kunta19 omitted and 1 obs not used.

note: 47.kunta19 != 0 predicts failure perfectly;
      47.kunta19 omitted and 2 obs not used.

note: 60.kunta19 != 0 predicts failure perfectly;
      60.kunta19 omitted and 1 obs not used.

note: 81.kunta19 != 0 predicts failure perfectly;
      81.kunta19 omitted and 1 obs not used.

note: 90.kunta19 != 0 predicts failure perfectly;
      90.kunta19 omitted and 6 obs not used.

note: 105.kunta19 != 0 predicts failure perfectly;
      105.kunta19 omitted and 4 obs not used.

note: 142.kunta19 != 0 predicts failure perfectly;
      142.kunta19 omitted and 1 obs not used.

note: 170.kunta19 != 0 predicts success perfectly;
      170.kunta19 omitted and 1 obs not used.

note: 181.kunta19 != 0 predicts failure perfectly;
      181.kunta19 omitted and 4 obs not used.

note: 213.kunta19 != 0 predicts failure perfectly;
      213.kunta19 omitted and 3 obs not used.

note: 218.kunta19 != 0 predicts failure perfectly;
      218.kunta19 omitted and 4 obs not used.

note: 256.kunta19 != 0 predicts failure perfectly;
      256.kunta19 omitted and 2 obs not used.

note: 284.kunta19 != 0 predicts failure perfectly;
      284.kunta19 omitted and 2 obs not used.

note: 312.kunta19 != 0 predicts failure perfectly;
      312.kunta19 omitted and 7 obs not used.

note: 316.kunta19 != 0 predicts failure perfectly;
      316.kunta19 omitted and 2 obs not used.

note: 320.kunta19 != 0 predicts failure perfectly;
      320.kunta19 omitted and 10 obs not used.

note: 407.kunta19 != 0 predicts failure perfectly;
      407.kunta19 omitted and 2 obs not used.

note: 416.kunta19 != 0 predicts failure perfectly;
      416.kunta19 omitted and 1 obs not used.

note: 480.kunta19 != 0 predicts failure perfectly;
      480.kunta19 omitted and 1 obs not used.

note: 538.kunta19 != 0 predicts failure perfectly;
      538.kunta19 omitted and 2 obs not used.

note: 616.kunta19 != 0 predicts failure perfectly;
      616.kunta19 omitted and 2 obs not used.

note: 619.kunta19 != 0 predicts failure perfectly;
      619.kunta19 omitted and 1 obs not used.

note: 631.kunta19 != 0 predicts failure perfectly;
      631.kunta19 omitted and 2 obs not used.

note: 681.kunta19 != 0 predicts failure perfectly;
      681.kunta19 omitted and 2 obs not used.

note: 700.kunta19 != 0 predicts failure perfectly;
      700.kunta19 omitted and 1 obs not used.

note: 736.kunta19 != 0 predicts failure perfectly;
      736.kunta19 omitted and 1 obs not used.

note: 739.kunta19 != 0 predicts failure perfectly;
      739.kunta19 omitted and 3 obs not used.

note: 742.kunta19 != 0 predicts failure perfectly;
      742.kunta19 omitted and 2 obs not used.

note: 751.kunta19 != 0 predicts failure perfectly;
      751.kunta19 omitted and 5 obs not used.

note: 890.kunta19 != 0 predicts failure perfectly;
      890.kunta19 omitted and 1 obs not used.

note: 918.kunta19 != 0 predicts failure perfectly;
      918.kunta19 omitted and 3 obs not used.

Iteration 0:   log pseudolikelihood = -30869.845  
Iteration 1:   log pseudolikelihood = -28915.102  
Iteration 2:   log pseudolikelihood = -28871.336  
Iteration 3:   log pseudolikelihood = -28871.112  
Iteration 4:   log pseudolikelihood = -28871.112  

Logistic regression                                     Number of obs = 49,599
                                                        Wald chi2(16) =      .
                                                        Prob > chi2   =      .
Log pseudolikelihood = -28871.112                       Pseudo R2     = 0.0647

                                (Std. err. adjusted for 259 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0465663   .0168361     2.77   0.006     .0135682    .0795644
     molincome |   .0195338   .0200766     0.97   0.331    -.0198156    .0588833
        female |   .5100468   .0260206    19.60   0.000     .4590474    .5610462
           age |   .0310502   .0038753     8.01   0.000     .0234548    .0386456
       foreign |  -.9755142    .061046   -15.98   0.000    -1.095162   -.8558663
               |
       moses1d |
            1  |   .8648083   .0942436     9.18   0.000     .6800942    1.049522
            2  |   .3839481   .0768425     5.00   0.000     .2333395    .5345566
            3  |   .6078663   .0575453    10.56   0.000     .4950796    .7206529
            4  |   .2619276   .0459451     5.70   0.000     .1718769    .3519782
            5  |   .0457967    .052978     0.86   0.387    -.0580383    .1496317
            6  |   .3656969   .0681304     5.37   0.000     .2321639      .49923
            7  |   .3730297    .072031     5.18   0.000     .2318516    .5142079
            9  |   .0550801   .0853348     0.65   0.519     -.112173    .2223331
               |
     firstvote |   .5850604   .0521386    11.22   0.000     .4828706    .6872501
1.mohighschool |   .6089998   .0255937    23.79   0.000     .5588371    .6591625
               |
       kunta19 |
            9  |   1.040746   .0076001   136.94   0.000      1.02585    1.055642
           10  |   .1499779   .0093247    16.08   0.000     .1317018    .1682539
           16  |   1.115092   .0221889    50.25   0.000     1.071602    1.158581
           18  |   .1746341   .0129877    13.45   0.000     .1491786    .2000896
           19  |  -.0879684   .0105231    -8.36   0.000    -.1085933   -.0673436
           20  |  -1.119888    .016378   -68.38   0.000    -1.151988   -1.087788
           43  |          0  (empty)
           46  |   .8880312   .0360149    24.66   0.000     .8174433    .9586191
           47  |          0  (empty)
           49  |   .1002246   .0159289     6.29   0.000     .0690045    .1314447
           50  |   .6761078   .0160395    42.15   0.000     .6446709    .7075446
           51  |   .1561652   .0043063    36.26   0.000     .1477249    .1646054
           52  |   .1031129   .0213109     4.84   0.000     .0613443    .1448815
           60  |          0  (empty)
           61  |  -.3656814   .0190906   -19.16   0.000    -.4030983   -.3282644
           69  |   .2937124    .007928    37.05   0.000     .2781737    .3092511
           71  |    .459793   .0054716    84.03   0.000     .4490688    .4705172
           72  |   .3462273   .0120592    28.71   0.000     .3225916    .3698629
           74  |   .3398435   .0065752    51.69   0.000     .3269562    .3527307
           75  |  -.6556922   .0185781   -35.29   0.000    -.6921047   -.6192798
           77  |  -.1594097   .0074606   -21.37   0.000    -.1740321   -.1447873
           78  |  -.2908769   .0162202   -17.93   0.000    -.3226679   -.2590859
           79  |  -.4233902    .007687   -55.08   0.000    -.4384565   -.4083239
           81  |          0  (empty)
           82  |  -.6133436   .0142397   -43.07   0.000     -.641253   -.5854342
           86  |  -.0680615   .0048018   -14.17   0.000    -.0774729   -.0586502
           90  |          0  (empty)
           91  |    .284324   .0164125    17.32   0.000     .2521561    .3164919
           92  |  -.1857628   .0116536   -15.94   0.000    -.2086035   -.1629222
           97  |   .9350487   .0256382    36.47   0.000     .8847987    .9852986
           98  |   .2425332   .0110033    22.04   0.000     .2209672    .2640993
           99  |  -.2883281   .0376958    -7.65   0.000    -.3622106   -.2144456
          102  |  -.0744813   .0162366    -4.59   0.000    -.1063044   -.0426582
          103  |    .321369     .04399     7.31   0.000     .2351503    .4075877
          105  |          0  (empty)
          106  |  -.0977551   .0157788    -6.20   0.000     -.128681   -.0668291
          108  |  -.0102848   .0058651    -1.75   0.080    -.0217801    .0012106
          109  |   .0790678   .0125189     6.32   0.000     .0545313    .1036044
          111  |  -.4082929   .0173891   -23.48   0.000     -.442375   -.3742108
          139  |   .4337842   .0174224    24.90   0.000      .399637    .4679315
          140  |  -.8242783   .0195356   -42.19   0.000    -.8625674   -.7859892
          142  |          0  (empty)
          143  |   .9504538   .0151711    62.65   0.000      .920719    .9801885
          145  |   .1334178   .0142469     9.36   0.000     .1054943    .1613412
          146  |  -.3145842   .0119193   -26.39   0.000    -.3379455   -.2912229
          148  |  -1.751543   .0142916  -122.56   0.000    -1.779554   -1.723532
          149  |   .5921568   .0289505    20.45   0.000     .5354148    .6488988
          151  |    .410412   .0096975    42.32   0.000     .3914052    .4294187
          152  |  -.9656823   .0183651   -52.58   0.000    -1.001677   -.9296874
          153  |   .5066628   .0194205    26.09   0.000     .4685994    .5447262
          165  |  -.2292326   .0041605   -55.10   0.000     -.237387   -.2210782
          167  |   .0261968    .013374     1.96   0.050    -.0000157    .0524093
          169  |   .0877096   .0061785    14.20   0.000        .0756    .0998193
          170  |          0  (empty)
          171  |   .4621861   .0161798    28.57   0.000     .4304743    .4938978
          172  |  -1.223254   .0214234   -57.10   0.000    -1.265243   -1.181265
          176  |  -.2339077   .0173494   -13.48   0.000    -.2679118   -.1999035
          177  |  -.9393814    .011171   -84.09   0.000    -.9612761   -.9174866
          178  |   .4318841   .0199517    21.65   0.000     .3927796    .4709886
          179  |   .1861628   .0137075    13.58   0.000     .1592966    .2130289
          181  |          0  (empty)
          182  |  -.2781456   .0084809   -32.80   0.000    -.2947679   -.2615234
          186  |  -.4569252   .0138603   -32.97   0.000    -.4840909   -.4297596
          202  |   .0604241     .00797     7.58   0.000     .0448032     .076045
          204  |   .3309444   .0287865    11.50   0.000     .2745239     .387365
          205  |  -.0407541   .0134901    -3.02   0.003    -.0671942    -.014314
          208  |   .1497239   .0127188    11.77   0.000     .1247956    .1746522
          211  |  -.1822361   .0111282   -16.38   0.000     -.204047   -.1604252
          213  |          0  (empty)
          214  |  -.0619139   .0119131    -5.20   0.000    -.0852632   -.0385646
          216  |  -.1525772   .0114268   -13.35   0.000    -.1749733   -.1301812
          217  |   .2337094   .0063476    36.82   0.000     .2212684    .2461505
          218  |          0  (empty)
          224  |   .0970872    .007654    12.68   0.000     .0820855    .1120888
          226  |   .0158979   .0078286     2.03   0.042     .0005542    .0312417
          230  |   .7584501   .0119199    63.63   0.000     .7350875    .7818128
          231  |  -.5035416   .0183169   -27.49   0.000     -.539442   -.4676412
          232  |  -.2652842   .0133471   -19.88   0.000    -.2914441   -.2391243
          233  |  -.0341183   .0141434    -2.41   0.016    -.0618389   -.0063977
          235  |   .1720652   .0181922     9.46   0.000     .1364091    .2077213
          236  |   .2100882   .0084616    24.83   0.000     .1935037    .2266727
          239  |   .5968871   .0145408    41.05   0.000     .5683877    .6253866
          240  |  -.0179575   .0116245    -1.54   0.122     -.040741    .0048261
          241  |  -2.161396   .0171906  -125.73   0.000    -2.195089   -2.127703
          244  |   .2850998   .0086242    33.06   0.000     .2681966     .302003
          245  |   -.077594   .0113701    -6.82   0.000     -.099879    -.055309
          249  |  -1.006505   .0135957   -74.03   0.000    -1.033152   -.9798579
          250  |  -.0872624    .024539    -3.56   0.000    -.1353581   -.0391668
          256  |          0  (empty)
          257  |   -.130083   .0077082   -16.88   0.000    -.1451908   -.1149751
          260  |  -.9270078   .0136206   -68.06   0.000    -.9537037   -.9003118
          261  |    .003532   .0180834     0.20   0.845    -.0319109    .0389748
          263  |  -.0225143   .0162959    -1.38   0.167    -.0544537    .0094251
          265  |   .0792184   .0212868     3.72   0.000     .0374971    .1209398
          271  |  -.4521831   .0144685   -31.25   0.000    -.4805409   -.4238254
          272  |  -.0839033   .0125341    -6.69   0.000    -.1084696   -.0593371
          273  |  -.2181816   .0229587    -9.50   0.000    -.2631797   -.1731834
          275  |  -.7729865   .0133349   -57.97   0.000    -.7991224   -.7468505
          276  |   .1395509   .0079338    17.59   0.000     .1240009    .1551009
          280  |   2.559636   .0315347    81.17   0.000      2.49783    2.621443
          284  |          0  (empty)
          285  |  -.0508033   .0139215    -3.65   0.000    -.0780888   -.0235177
          286  |   .1158147   .0133375     8.68   0.000     .0896737    .1419558
          287  |   .7505114   .0079344    94.59   0.000     .7349602    .7660625
          288  |    .722822   .0154673    46.73   0.000     .6925066    .7531374
          290  |   1.587599   .0089528   177.33   0.000     1.570052    1.605146
          291  |   1.689587   .0185099    91.28   0.000     1.653308    1.725865
          297  |   .3068545   .0168689    18.19   0.000      .273792     .339917
          300  |   .1925598   .0062289    30.91   0.000     .1803512    .2047683
          301  |  -.1608837   .0152664   -10.54   0.000    -.1908053   -.1309622
          304  |   .6907719   .0133371    51.79   0.000     .6646317     .716912
          305  |  -.5127444    .007591   -67.55   0.000    -.5276226   -.4978663
          309  |  -.4251201   .0092066   -46.18   0.000    -.4431647   -.4070755
          312  |          0  (empty)
          316  |          0  (empty)
          317  |  -.1299248   .0126285   -10.29   0.000    -.1546762   -.1051734
          320  |          0  (empty)
          322  |  -.3000273   .0133264   -22.51   0.000    -.3261467    -.273908
          398  |  -.2555277   .0165911   -15.40   0.000    -.2880456   -.2230098
          399  |  -1.466413   .0184455   -79.50   0.000    -1.502566   -1.430261
          400  |  -.5019422   .0192686   -26.05   0.000    -.5397079   -.4641764
          402  |  -.3366215   .0083381   -40.37   0.000     -.352964   -.3202791
          403  |   .3908464   .0091311    42.80   0.000     .3729498    .4087431
          405  |   .4887281   .0121317    40.29   0.000     .4649504    .5125058
          407  |          0  (empty)
          408  |  -.3642818   .0086992   -41.88   0.000    -.3813318   -.3472318
          410  |  -.0257359   .0083165    -3.09   0.002    -.0420359   -.0094358
          416  |          0  (empty)
          418  |    .081502   .0108579     7.51   0.000      .060221     .102783
          420  |   1.084268    .019631    55.23   0.000     1.045792    1.122744
          422  |  -.2756868   .0073668   -37.42   0.000    -.2901255   -.2612481
          423  |   .5506181   .0138805    39.67   0.000     .5234129    .5778233
          425  |   .4489328   .0058066    77.31   0.000     .4375521    .4603135
          426  |  -.1607159   .0089059   -18.05   0.000     -.178171   -.1432607
          430  |  -.0908685   .0109183    -8.32   0.000     -.112268    -.069469
          433  |  -.5616696   .0151336   -37.11   0.000    -.5913309   -.5320084
          434  |   -.463854   .0156804   -29.58   0.000     -.494587    -.433121
          436  |   .6994338   .0056289   124.26   0.000     .6884014    .7104663
          440  |     .75023   .0227465    32.98   0.000     .7056476    .7948124
          441  |   .8892187   .0091773    96.89   0.000     .8712315     .907206
          444  |  -.2638833    .005696   -46.33   0.000    -.2750473   -.2527194
          445  |    .294514   .0077383    38.06   0.000     .2793472    .3096808
          475  |   .7826776   .0098464    79.49   0.000      .763379    .8019762
          480  |          0  (empty)
          481  |   .1306348   .0065133    20.06   0.000     .1178691    .1434006
          483  |   1.195212   .0166988    71.57   0.000     1.162483    1.227941
          484  |  -1.183033   .0188072   -62.90   0.000    -1.219895   -1.146172
          489  |    .957321   .0167565    57.13   0.000     .9244788    .9901632
          491  |  -.0668756   .0137249    -4.87   0.000    -.0937759   -.0399753
          494  |   .6407775   .0075131    85.29   0.000     .6260522    .6555029
          495  |   .5843828   .0340165    17.18   0.000     .5177118    .6510539
          498  |   .4728504   .0204386    23.14   0.000     .4327915    .5129093
          499  |  -.3965674   .0129568   -30.61   0.000    -.4219621   -.3711726
          500  |     .21676   .0050126    43.24   0.000     .2069356    .2265845
          503  |  -.3435848   .0077679   -44.23   0.000    -.3588097     -.32836
          505  |   .0751932   .0155406     4.84   0.000     .0447343    .1056522
          507  |  -.0382311   .0087495    -4.37   0.000    -.0553798   -.0210823
          508  |  -.4360739   .0137805   -31.64   0.000    -.4630831   -.4090646
          529  |  -.3630186   .0168814   -21.50   0.000    -.3961054   -.3299317
          531  |  -.4303568   .0058674   -73.35   0.000    -.4418567   -.4188569
          535  |    .567403   .0063337    89.58   0.000     .5549892    .5798168
          536  |  -.3542232   .0088707   -39.93   0.000    -.3716094    -.336837
          538  |          0  (empty)
          541  |  -.3198157   .0142797   -22.40   0.000    -.3478034    -.291828
          543  |  -.0524373   .0074501    -7.04   0.000    -.0670392   -.0378354
          545  |   .8046854   .0125047    64.35   0.000     .7801765    .8291942
          560  |  -1.519874   .0192097   -79.12   0.000    -1.557524   -1.482224
          562  |  -.0083858   .0137364    -0.61   0.542    -.0353085     .018537
          563  |   .0815985   .0140438     5.81   0.000     .0540731     .109124
          564  |   .2319113   .0122396    18.95   0.000     .2079222    .2559004
          577  |  -.3548166   .0057161   -62.07   0.000    -.3660199   -.3436133
          578  |   .1632799   .0128538    12.70   0.000     .1380869     .188473
          580  |   1.955777   .0154582   126.52   0.000      1.92548    1.986075
          581  |   .1933306   .0102562    18.85   0.000     .1732288    .2134323
          583  |   .2393764   .0130363    18.36   0.000     .2138258    .2649271
          584  |   .8592507   .0148175    57.99   0.000     .8302089    .8882925
          588  |   .3661397   .0101191    36.18   0.000     .3463066    .3859728
          592  |   .1460338   .0096081    15.20   0.000     .1272022    .1648654
          593  |  -.8276999   .0081503  -101.55   0.000    -.8436742   -.8117257
          595  |  -.8509357   .0205019   -41.51   0.000    -.8911186   -.8107528
          598  |   .3139538   .0156276    20.09   0.000     .2833241    .3445834
          599  |  -.5171527   .0148591   -34.80   0.000     -.546276   -.4880293
          601  |   .4834345   .0079813    60.57   0.000     .4677915    .4990774
          604  |   .3644156   .0123659    29.47   0.000     .3401788    .3886524
          607  |  -.0148792   .0137146    -1.08   0.278    -.0417593     .012001
          608  |    .193531   .0085424    22.66   0.000     .1767882    .2102737
          609  |    .079332   .0108957     7.28   0.000     .0579767    .1006873
          611  |  -.0064216   .0275101    -0.23   0.815    -.0603404    .0474973
          614  |  -.3433616    .010639   -32.27   0.000    -.3642136   -.3225095
          615  |  -.0690114   .0134958    -5.11   0.000    -.0954627   -.0425602
          616  |          0  (empty)
          619  |          0  (empty)
          620  |  -.3841103   .0091545   -41.96   0.000    -.4020528   -.3661679
          623  |   .0972043   .0118047     8.23   0.000     .0740676     .120341
          624  |  -.4607772   .0080897   -56.96   0.000    -.4766328   -.4449216
          625  |  -.2037063    .013479   -15.11   0.000    -.2301246    -.177288
          626  |   .1255134   .0091294    13.75   0.000     .1076202    .1434066
          630  |   1.077639   .0183519    58.72   0.000      1.04167    1.113608
          631  |          0  (empty)
          635  |    .513903   .0047105   109.10   0.000     .5046706    .5231354
          636  |  -.9190927   .0159407   -57.66   0.000    -.9503359   -.8878495
          638  |  -.2562703   .0104705   -24.48   0.000    -.2767922   -.2357484
          678  |   -.069194   .0141186    -4.90   0.000     -.096866    -.041522
          680  |  -.2403004   .0086987   -27.62   0.000    -.2573495   -.2232513
          681  |          0  (empty)
          683  |   .7942832   .0068269   116.35   0.000     .7809028    .8076636
          684  |  -.1576978   .0174632    -9.03   0.000    -.1919251   -.1234706
          686  |   1.135545   .0332832    34.12   0.000     1.070311    1.200779
          687  |   .4146161   .0235541    17.60   0.000      .368451    .4607812
          689  |   1.121721   .0183969    60.97   0.000     1.085663    1.157778
          691  |   .4862373   .0098075    49.58   0.000      .467015    .5054597
          694  |  -.6354831   .0119732   -53.08   0.000    -.6589501   -.6120161
          697  |  -.1726754   .0141824   -12.18   0.000    -.2004724   -.1448783
          698  |  -.0732264   .0140253    -5.22   0.000    -.1007155   -.0457373
          700  |          0  (empty)
          702  |   .2332786   .0269085     8.67   0.000     .1805389    .2860183
          704  |  -.6278667   .0122443   -51.28   0.000     -.651865   -.6038683
          707  |   .1901822   .0217743     8.73   0.000     .1475054    .2328591
          710  |   -.157482   .0138217   -11.39   0.000    -.1845721    -.130392
          729  |  -.1196503   .0088984   -13.45   0.000    -.1370909   -.1022097
          732  |  -.0195226   .0166517    -1.17   0.241    -.0521594    .0131142
          734  |  -.0293324   .0061626    -4.76   0.000    -.0414109   -.0172539
          736  |          0  (empty)
          738  |  -.3645531   .0285554   -12.77   0.000    -.4205207   -.3085855
          739  |          0  (empty)
          740  |  -.0507082   .0106093    -4.78   0.000     -.071502   -.0299145
          742  |          0  (empty)
          743  |   .3487829   .0135772    25.69   0.000     .3221722    .3753937
          746  |   .4275839   .0050571    84.55   0.000     .4176722    .4374956
          747  |   .2390131   .0178174    13.41   0.000     .2040917    .2739346
          748  |   1.362255   .0137242    99.26   0.000     1.335356    1.389154
          749  |   .2184248   .0147023    14.86   0.000     .1896088    .2472408
          751  |          0  (empty)
          753  |  -.0032455    .007979    -0.41   0.684     -.018884    .0123931
          755  |  -1.253051   .0155327   -80.67   0.000    -1.283495   -1.222608
          758  |  -.7536543   .0110427   -68.25   0.000    -.7752975    -.732011
          759  |   .2081876   .0092127    22.60   0.000      .190131    .2262442
          761  |  -.0202573   .0068433    -2.96   0.003      -.03367   -.0068446
          762  |  -.7522918   .0126503   -59.47   0.000     -.777086   -.7274976
          765  |  -.2610012   .0077016   -33.89   0.000    -.2760961   -.2459064
          768  |   .4147222   .0087318    47.50   0.000     .3976082    .4318361
          777  |   1.337356   .0189484    70.58   0.000     1.300218    1.374494
          778  |  -.7684039   .0118173   -65.02   0.000    -.7915653   -.7452425
          781  |   .1223245    .015153     8.07   0.000     .0926252    .1520238
          783  |   .3414011   .0134197    25.44   0.000      .315099    .3677033
          785  |   .4606474   .0139351    33.06   0.000      .433335    .4879597
          790  |  -.4118064   .0079488   -51.81   0.000    -.4273857   -.3962272
          791  |  -.0721511   .0147818    -4.88   0.000    -.1011229   -.0431792
          831  |   .5370892   .0202213    26.56   0.000     .4974563    .5767222
          832  |   .8425939   .0192601    43.75   0.000     .8048448    .8803429
          834  |  -.4068874   .0243484   -16.71   0.000    -.4546095   -.3591653
          837  |   .4019294   .0137255    29.28   0.000     .3750279    .4288308
          845  |   -.095029   .0074465   -12.76   0.000    -.1096238   -.0804341
          846  |   .4064251   .0241688    16.82   0.000     .3590552     .453795
          848  |  -.5422013   .0120232   -45.10   0.000    -.5657663   -.5186362
          849  |     .64685   .0079059    81.82   0.000     .6313548    .6623453
          850  |   .1827712   .0158021    11.57   0.000     .1517996    .2137427
          851  |     .12548   .0174178     7.20   0.000     .0913416    .1596183
          853  |   .3510913   .0170268    20.62   0.000     .3177194    .3844632
          854  |  -.3932109   .0134042   -29.33   0.000    -.4194827   -.3669392
          857  |   1.503824   .0240052    62.65   0.000     1.456774    1.550873
          858  |  -.1729738   .0170816   -10.13   0.000    -.2064532   -.1394943
          859  |     .95313   .0148159    64.33   0.000     .9240913    .9821687
          886  |  -.6880222   .0111646   -61.63   0.000    -.7099045     -.66614
          887  |  -.1440322   .0408834    -3.52   0.000    -.2241622   -.0639022
          889  |   .2974904   .0075557    39.37   0.000     .2826815    .3122992
          890  |          0  (empty)
          892  |  -.2428493   .0169788   -14.30   0.000    -.2761272   -.2095714
          893  |  -.2344274   .0139833   -16.76   0.000    -.2618342   -.2070207
          895  |  -.1121311    .008536   -13.14   0.000    -.1288613   -.0954009
          905  |   .1695395   .0100712    16.83   0.000     .1498004    .1892787
          908  |  -.3684768   .0080558   -45.74   0.000    -.3842659   -.3526877
          915  |   .0215976     .01332     1.62   0.105     -.004509    .0477043
          918  |          0  (empty)
          922  |  -.8717904   .0172321   -50.59   0.000    -.9055647   -.8380161
          924  |    .988038   .0267216    36.98   0.000     .9356647    1.040411
          925  |   -.664442   .0214149   -31.03   0.000    -.7064144   -.6224697
          927  |  -.2131098   .0069771   -30.54   0.000    -.2267847   -.1994349
          931  |  -.4176575    .009178   -45.51   0.000    -.4356461    -.399669
          934  |  -1.279198   .0118811  -107.67   0.000    -1.302485   -1.255912
          935  |   .5772128   .0054983   104.98   0.000     .5664364    .5879893
          936  |  -.3614544   .0164199   -22.01   0.000    -.3936369    -.329272
          946  |   .8266275   .0062352   132.58   0.000     .8144068    .8388482
          976  |  -.2230419   .0159754   -13.96   0.000    -.2543531   -.1917308
          977  |   .1060346   .0093065    11.39   0.000     .0877942     .124275
          980  |   .4581648   .0089871    50.98   0.000     .4405504    .4757792
          981  |  -.1326619   .0137638    -9.64   0.000    -.1596385   -.1056854
          989  |   -.765234   .0069972  -109.36   0.000    -.7789483   -.7515198
          992  |  -.7355502   .0127686   -57.61   0.000    -.7605762   -.7105242
               |
         _cons |  -2.587092   .2122414   -12.19   0.000    -3.003077   -2.171106
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "Yes"

added macro:
          e(Controls5) : "Yes"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                         Number of obs = 19,860

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3086606    .003278      .3022355    .3150858
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M5

. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local numbers "& (1) & (2) & (3) & (4) & (5) \\ \hline"

. local emptyrow "& & & & &  \\ "

. local line "& & & & & \hline \\ "

. 
. local dstars " "

. local sestars " "

. esttab, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("Controls Controls" "Controls1 \phantom{controls}"  "Controls2 \phantom{controls}" "Controls3 \phantom{c
> ontrols}" "Controls4 \phantom{controls}"  "Controls5 Municipality FE" "umean Untreated $\bar{Y}$" "N Observation
> s") ///
> sfmt(%9.3f %9.3f %9.3f %9.3f  %9.3f  %9.3f  %9.3f %9.0fc) mlabels(none) nonumbers posthead("`header'" "`emptyrow
> '" "`numbers'" "`emptyrow'") ///
> refcat(treated "", nolabel below) title("Average Treatment Effect - Logit Model") nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

Average Treatment Effect - Logit Model
----------------------------------------------------------------------------------------------------
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & & &  \\ 
& (1) & (2) & (3) & (4) & (5) \\ \hline
& & & & &  \\ 
voted22                                                                                             
Treatments Pooled           0.041***        0.042***        0.045***        0.042***        0.047***
                          (0.015)         (0.016)         (0.016)         (0.016)         (0.017)   

                                                                                                    
----------------------------------------------------------------------------------------------------
Controls                       No    Female, Age, Immigrant,    Female, Age, Immigrant,              No    Female,
>  Age, Immigrant,   
\phantom{controls}                      Ln Income      Ln Income,                      Ln Income,   
\phantom{controls}                                   SES Background,                    SES Background,   
\phantom{controls}                                   Educational Background,                    Educational Backgr
> ound,   
\phantom{controls}                                   First Time Eligble to Vote                    First Time Elig
> ble to Vote   
Municipality FE                No              No              No             Yes             Yes   
Untreated $\bar{Y}$         0.307           0.308           0.308           0.308           0.309   
Observations               50,140          49,679          49,679          50,063          49,599   
----------------------------------------------------------------------------------------------------

. 
. ****************************************************************************************************************
> ************************
. 
. 
. *************************************************
. *****TABLE 4
. *************************************************
. 
. 
. eststo clear

. 
. label variable treated "Treated"

. 
. 
. eststo pooled: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(kunt
> a19)

Linear regression                               Number of obs     =     49,679
                                                F(15, 289)        =     303.74
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0622
                                                Root MSE          =     .44939

                                (Std. err. adjusted for 290 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0090149   .0033085     2.72   0.007     .0025032    .0155267
     molincome |   .0026971   .0038687     0.70   0.486    -.0049173    .0103114
        female |   .1044478   .0064625    16.16   0.000     .0917282    .1171674
           age |   .0075646   .0010094     7.49   0.000     .0055779    .0095513
       foreign |  -.1427179   .0087355   -16.34   0.000    -.1599111   -.1255246
               |
       moses1d |
            1  |   .1897906   .0226286     8.39   0.000     .1452528    .2343283
            2  |   .0721931   .0146617     4.92   0.000     .0433358    .1010504
            3  |   .1282275   .0102864    12.47   0.000     .1079819    .1484732
            4  |    .045854   .0075732     6.05   0.000     .0309485    .0607596
            5  |    .005741    .009352     0.61   0.540    -.0126658    .0241477
            6  |   .0720807   .0117937     6.11   0.000     .0488683    .0952931
            7  |   .0707205   .0129955     5.44   0.000     .0451428    .0962983
            9  |   .0071824   .0140908     0.51   0.611    -.0205513     .034916
               |
     firstvote |   .1234276   .0110779    11.14   0.000      .101624    .1452311
1.mohighschool |   .1319606   .0073446    17.97   0.000     .1175049    .1464162
         _cons |  -.0370493   .0395638    -0.94   0.350     -.114919    .0408205
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store pooled

. 
. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: pooled

. 
. qui: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated1!=., cluster
> (kunta19)

. 
. *Store coefficient, variance and degrees of freedom for group difference calulation
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated2!=., cluster
> (kunta19)

. 
. *Store coefficient, variance and degrees of freedom for group difference calulation
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. *Calculate group difference
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo neutral: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated1!
> =., cluster(kunta19)

Linear regression                               Number of obs     =     29,799
                                                F(15, 278)        =     222.60
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0625
                                                Root MSE          =     .44921

                                (Std. err. adjusted for 279 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0164503   .0052129     3.16   0.002     .0061884    .0267121
     molincome |   .0063795   .0042816     1.49   0.137    -.0020491     .014808
        female |   .1085384   .0071379    15.21   0.000     .0944871    .1225897
           age |   .0073586   .0010374     7.09   0.000     .0053165    .0094008
       foreign |   -.132838   .0098657   -13.46   0.000     -.152259    -.113417
               |
       moses1d |
            1  |   .1771278   .0300232     5.90   0.000     .1180262    .2362295
            2  |   .0688269    .021098     3.26   0.001     .0272948    .1103591
            3  |   .1197545    .018326     6.53   0.000     .0836791    .1558299
            4  |   .0428269   .0115109     3.72   0.000     .0201673    .0654864
            5  |   .0000423   .0126769     0.00   0.997    -.0249126    .0249971
            6  |   .0627628    .016469     3.81   0.000     .0303431    .0951826
            7  |   .0712609   .0165714     4.30   0.000     .0386395    .1038823
            9  |   .0076691   .0184938     0.41   0.679    -.0287366    .0440748
               |
     firstvote |   .1238196   .0142545     8.69   0.000     .0957591      .15188
1.mohighschool |   .1333075   .0095359    13.98   0.000     .1145357    .1520792
         _cons |  -.0679647   .0480704    -1.41   0.159    -.1625929    .0266636
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Neutral"

added macro:
             e(label1) : "Neutral"

. estadd local label2 "- Expressive"

added macro:
             e(label2) : "- Expressive"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store neutral

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: neutral

. 
. qui: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated2!=., cluster
> (kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated3!=., cluster
> (kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo expressive: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if treate
> d2!=., cluster(kunta19)

Linear regression                               Number of obs     =     29,806
                                                F(15, 280)        =     229.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0627
                                                Root MSE          =     .44844

                                (Std. err. adjusted for 281 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0090378   .0048131     1.88   0.061    -.0004367    .0185124
     molincome |   .0046613   .0045422     1.03   0.306    -.0042798    .0136025
        female |   .1000766   .0081635    12.26   0.000     .0840069    .1161463
           age |   .0072795   .0011327     6.43   0.000     .0050497    .0095092
       foreign |  -.1393905   .0096427   -14.46   0.000    -.1583718   -.1204091
               |
       moses1d |
            1  |    .190978    .030057     6.35   0.000     .1318116    .2501444
            2  |   .0673013   .0190488     3.53   0.000     .0298041    .1047984
            3  |   .1192875   .0142766     8.36   0.000     .0911844    .1473906
            4  |   .0411241   .0091908     4.47   0.000     .0230323    .0592159
            5  |   .0000547   .0116732     0.00   0.996    -.0229237    .0230331
            6  |   .0707636   .0149219     4.74   0.000     .0413902    .1001369
            7  |   .0707052   .0171496     4.12   0.000     .0369467    .1044637
            9  |  -.0166019   .0217388    -0.76   0.446    -.0593942    .0261904
               |
     firstvote |   .1260157    .014187     8.88   0.000     .0980891    .1539424
1.mohighschool |   .1361952   .0091743    14.85   0.000     .1181358    .1542545
         _cons |  -.0458276   .0448277    -1.02   0.308    -.1340697    .0424146
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Expressive"

added macro:
             e(label1) : "Expressive"

. estadd local label2 "- Informative"

added macro:
             e(label2) : "- Informative"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store expressive

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: expressive

. 
. 
. qui: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated3!=., cluster
> (kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated1!=., cluster
> (kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo informative: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if treat
> ed3!=., cluster(kunta19)

Linear regression                               Number of obs     =     29,832
                                                F(15, 276)        =     169.23
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0604
                                                Root MSE          =     .44814

                                (Std. err. adjusted for 277 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0016689    .004057     0.41   0.681    -.0063178    .0096555
     molincome |   .0026309   .0044665     0.59   0.556    -.0061618    .0114236
        female |   .1022253    .007969    12.83   0.000     .0865376     .117913
           age |   .0085025   .0011021     7.72   0.000      .006333     .010672
       foreign |  -.1331532   .0094628   -14.07   0.000    -.1517817   -.1145248
               |
       moses1d |
            1  |   .1802064   .0283215     6.36   0.000     .1244528    .2359601
            2  |   .0661445   .0162248     4.08   0.000     .0342043    .0980847
            3  |   .1199458   .0119144    10.07   0.000     .0964912    .1434005
            4  |   .0379626   .0101167     3.75   0.000     .0180469    .0578783
            5  |  -.0064457   .0104957    -0.61   0.540    -.0271075     .014216
            6  |   .0584022   .0152239     3.84   0.000     .0284326    .0883719
            7  |   .0689885   .0154609     4.46   0.000     .0385522    .0994248
            9  |  -.0130944   .0198831    -0.66   0.511    -.0522361    .0260473
               |
     firstvote |   .1305827   .0154853     8.43   0.000     .1000985    .1610669
1.mohighschool |   .1278063   .0101621    12.58   0.000     .1078013    .1478113
         _cons |  -.0460036   .0440728    -1.04   0.297    -.1327651     .040758
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Informative"

added macro:
             e(label1) : "Informative"

. estadd local label2 "- Neutral"

added macro:
             e(label2) : "- Neutral"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store informative

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: informative

. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 `"Treatment: & Pooled & "Neutral" & "Expressive" & "Informative"\\ "'

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. esttab, keep (treated*) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers posthead("`header'" "`emptyrow'" `"`titles1'"' "`numbers'
> " "`emptyrow'") ///
> refcat(treated "", nolabel below) title("Different Treatments") nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

Different Treatments
------------------------------------------------------------------------------------
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & &  \\ 
Treatment: & Pooled & "Neutral" & "Expressive" & "Informative"\\ 
& (1) & (2) & (3) & (4) \\ \hline
& & & &  \\ 
Treated                     0.009***        0.016***        0.009*          0.002   
                          (0.003)         (0.005)         (0.005)         (0.004)   

                                                                                    
------------------------------------------------------------------------------------
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.308           0.308           0.308           0.308   
Observations               49,679          29,799          29,806          29,832   
\hline                                    Neutral      Expressive     Informative   
\phantom{label2}                     - Expressive    - Informative       - Neutral   
Differences                                 0.007           0.007        -0.015**   
\phantom{se}                              (0.007)         (0.006)         (0.007)   
------------------------------------------------------------------------------------

. 
. *************************************************
. *****TABLE 5
. *************************************************
. 
. label variable treatedf "Treated in HH"

. 
. eststo clear

. eststo M1: reg voted22 treatedf, cluster(kunta19)

Linear regression                               Number of obs     =     37,207
                                                F(1, 259)         =       4.77
                                                Prob > F          =     0.0299
                                                R-squared         =     0.0002
                                                Root MSE          =     .49996

                              (Std. err. adjusted for 260 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    treatedf |   .0136188   .0062358     2.18   0.030     .0013394    .0258982
       _cons |   .4943957   .0075109    65.82   0.000     .4796054     .509186
------------------------------------------------------------------------------

. estadd local Controls "No"

added macro:
           e(Controls) : "No"

. estadd local Controls1 " "

added macro:
          e(Controls1) : " "

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedf==0

Mean estimation                         Number of obs = 14,810

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .4943957   .0041085      .4863426    .5024488
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M1

. eststo M2: reg voted22 treatedf molincome female age foreign, cluster(kunta19)

Linear regression                               Number of obs     =     36,876
                                                F(5, 259)         =     301.23
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0942
                                                Root MSE          =      .4759

                              (Std. err. adjusted for 260 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    treatedf |   .0137912   .0053331     2.59   0.010     .0032894     .024293
   molincome |   .0671655   .0046638    14.40   0.000     .0579818    .0763492
      female |   .0493548   .0039503    12.49   0.000      .041576    .0571335
         age |   .0081935    .000296    27.68   0.000     .0076107    .0087764
     foreign |  -.3030646   .0151788   -19.97   0.000    -.3329541   -.2731751
       _cons |  -.5428431    .060792    -8.93   0.000    -.6625526   -.4231337
------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income"

added macro:
          e(Controls1) : "Ln Income"

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedf==0

Mean estimation                         Number of obs = 14,662

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |    .495976   .0041293      .4878821    .5040699
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M2

. eststo M3:  reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(kunta1
> 9)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     36,876
                                                F(14, 259)        =     612.45
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1407
                                                Root MSE          =     .46358

                                (Std. err. adjusted for 260 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0130222   .0055449     2.35   0.020     .0021033     .023941
     molincome |   .0248605   .0040409     6.15   0.000     .0169033    .0328177
        female |   .0095634   .0045088     2.12   0.035     .0006849     .018442
           age |   .0086664   .0002753    31.48   0.000     .0081243    .0092085
       foreign |  -.2464759   .0128641   -19.16   0.000    -.2718074   -.2211443
               |
       moses1d |
            1  |   .2246265   .0207849    10.81   0.000     .1836976    .2655553
            2  |   .0959936   .0167937     5.72   0.000     .0629242    .1290631
            3  |   .1767397     .01162    15.21   0.000      .153858    .1996215
            4  |   .0857361   .0117858     7.27   0.000     .0625278    .1089443
            5  |   .0027727   .0104861     0.26   0.792    -.0178761    .0234216
            6  |   .1327165   .0162459     8.17   0.000     .1007256    .1647075
            7  |   .0146583   .0174838     0.84   0.403    -.0197702    .0490868
            9  |   .0622016   .0236856     2.63   0.009     .0155608    .1088425
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1455762   .0079108    18.40   0.000     .1299985    .1611539
         _cons |  -.2481974   .0485264    -5.11   0.000    -.3437539   -.1526408
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedf==0

Mean estimation                         Number of obs = 14,662

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |    .495976   .0041293      .4878821    .5040699
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M3

. eststo M4:  reg voted22 treatedf i.kunta19, cluster(kunta19)

Linear regression                               Number of obs     =     37,207
                                                F(0, 259)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0206
                                                Root MSE          =     .49656

                              (Std. err. adjusted for 260 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    treatedf |   .0109524   .0059689     1.83   0.068    -.0008014    .0227062
             |
     kunta19 |
          9  |   .0581908   .0001198   485.82   0.000     .0579549    .0584267
         10  |  -.0945216   .0004495  -210.26   0.000    -.0954068   -.0936364
         16  |   .4642348    .002176   213.35   0.000     .4599499    .4685196
         18  |   .4642348    .002176   213.35   0.000     .4599499    .4685196
         19  |  -.1338983   .0005716  -234.24   0.000    -.1350239   -.1327726
         20  |  -.3866496   .0012349  -313.11   0.000    -.3890812    -.384218
         47  |   .4642348    .002176   213.35   0.000     .4599499    .4685196
         49  |  -.1425078   .0000631 -2259.56   0.000     -.142632   -.1423836
         50  |   .0664252   .0009822    67.63   0.000     .0644912    .0683593
         51  |   .0271092    .000114   237.86   0.000     .0268848    .0273336
         61  |  -.2560683   .0016224  -157.83   0.000    -.2592631   -.2528734
         69  |    .017097    .000263    65.02   0.000     .0165792    .0176148
         71  |   .0804927   .0005064   158.94   0.000     .0794955    .0814899
         72  |   .1110035   .0000442  2511.18   0.000     .1109164    .1110905
         74  |   .0443069   .0001754   252.56   0.000     .0439614    .0446524
         75  |   .1309014    .002176    60.16   0.000     .1266166    .1351863
         77  |   .0088348    .000331    26.69   0.000      .008183    .0094865
         78  |  -.5248129    .003793  -138.36   0.000    -.5322818   -.5173439
         79  |  -.0737744   .0003005  -245.50   0.000    -.0743661   -.0731826
         81  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
         82  |   .4642348    .002176   213.35   0.000     .4599499    .4685196
         86  |  -.0599464   .0001534  -390.85   0.000    -.0602485   -.0596444
         90  |   .1309014    .002176    60.16   0.000     .1266166    .1351863
         91  |  -.1665031   .0000446 -3736.69   0.000    -.1665908   -.1664154
         92  |  -.0767743   .0002619  -293.15   0.000      -.07729   -.0762586
         98  |  -.0302891   .0008085   -37.46   0.000    -.0318811    -.028697
         99  |  -.5302891   .0008085  -655.89   0.000    -.5318811    -.528697
        102  |   .1309014    .002176    60.16   0.000     .1266166    .1351863
        105  |   .4642348    .002176   213.35   0.000     .4599499    .4685196
        106  |  -.2813843   .0002116 -1329.75   0.000     -.281801   -.2809676
        108  |  -.0074936   .0002558   -29.29   0.000    -.0079974   -.0069899
        109  |  -.2050152   .0009577  -214.07   0.000    -.2069012   -.2031293
        111  |   .2169728   .0006837   317.34   0.000     .2156265    .2183192
        139  |  -.0864531   .0004769  -181.28   0.000    -.0873922    -.085514
        140  |  -.3786462   .0017038  -222.23   0.000    -.3820013   -.3752911
        143  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        146  |  -.2086444   .0000279 -7465.40   0.000    -.2086994   -.2085893
        148  |  -.0782328   .0005481  -142.74   0.000     -.079312   -.0771535
        151  |   .0865984   .0001031   840.10   0.000     .0863954    .0868014
        152  |   .4642348    .002176   213.35   0.000     .4599499    .4685196
        153  |  -.1914795    .003793   -50.48   0.000    -.1989485   -.1840106
        165  |  -.0193404   .0003385   -57.13   0.000     -.020007   -.0186739
        167  |  -.0724239   .0000695 -1041.90   0.000    -.0725607    -.072287
        169  |   .0322591     .00011   293.24   0.000     .0320424    .0324757
        171  |  -.5248129    .003793  -138.36   0.000    -.5322818   -.5173439
        176  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        177  |   .0581908   .0001198   485.82   0.000     .0579549    .0584267
        178  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        179  |  -.0280381   .0001982  -141.47   0.000    -.0284284   -.0276478
        181  |  -.5284637   .0018033  -293.05   0.000    -.5320147   -.5249126
        182  |  -.0719696   .0004998  -144.01   0.000    -.0729537   -.0709855
        186  |  -.1401832   .0014717   -95.25   0.000    -.1430813   -.1372852
        202  |  -.0074544    .000114   -65.39   0.000    -.0076789   -.0072299
        204  |   .4697109   .0008085   580.97   0.000     .4681189     .471303
        205  |  -.1958989   .0013845  -141.50   0.000    -.1986251   -.1931727
        208  |  -.1024999   .0003821  -268.22   0.000    -.1032525   -.1017474
        211  |  -.2082384   .0005197  -400.70   0.000    -.2092617    -.207215
        213  |   .1309014    .002176    60.16   0.000     .1266166    .1351863
        214  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        216  |  -.1552891   .0008085  -192.07   0.000    -.1568811    -.153697
        217  |   .0198125   .0000624   317.59   0.000     .0196896    .0199353
        218  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        224  |  -.1002275   .0005664  -176.97   0.000    -.1013428   -.0991123
        226  |   .0029823    .000277    10.77   0.000     .0024368    .0035278
        230  |   .0436617   .0004561    95.73   0.000     .0427636    .0445599
        231  |  -.0543594   .0006664   -81.57   0.000    -.0556716   -.0530471
        232  |  -.1987811   .0001863 -1066.87   0.000     -.199148   -.1984142
        233  |  -.1291938   .0014054   -91.93   0.000    -.1319613   -.1264264
        235  |  -.5248129    .003793  -138.36   0.000    -.5322818   -.5173439
        236  |   .0514014   .0000868   591.92   0.000     .0512304    .0515724
        239  |   .2059766   .0004943   416.66   0.000     .2050032    .2069501
        240  |  -.0641572   .0006526   -98.31   0.000    -.0654423   -.0628721
        241  |  -.1987811   .0001863 -1066.87   0.000     -.199148   -.1984142
        244  |   .0079737   .0000348   229.46   0.000     .0079053    .0080422
        245  |   -.039601   .0002335  -169.59   0.000    -.0400608   -.0391412
        249  |  -.1313843   .0002116  -620.89   0.000     -.131801   -.1309676
        250  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        256  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        257  |   .0464731   .0000497   934.35   0.000     .0463752    .0465711
        260  |  -.3270033   .0025992  -125.81   0.000    -.3321216   -.3218851
        261  |  -.1969557   .0008085  -243.61   0.000    -.1985478   -.1953637
        263  |  -.5302891   .0008085  -655.89   0.000    -.5318811    -.528697
        265  |  -.1477799   .0003394  -435.45   0.000    -.1484482   -.1471116
        271  |  -.1357652    .002176   -62.39   0.000    -.1400501   -.1314804
        272  |  -.0953745   .0002728  -349.58   0.000    -.0959118   -.0948373
        273  |  -.0248129    .003793    -6.54   0.000    -.0322818   -.0173439
        275  |  -.5248129    .003793  -138.36   0.000    -.5322818   -.5173439
        276  |  -.0685768   .0001269  -540.56   0.000    -.0688266    -.068327
        284  |  -.5248129    .003793  -138.36   0.000    -.5322818   -.5173439
        285  |  -.2371378   .0002818  -841.42   0.000    -.2376928   -.2365828
        286  |  -.0654478   .0001863  -351.26   0.000    -.0658147   -.0650809
        287  |   .1111884   .0003144   353.67   0.000     .1105693    .1118075
        288  |   .2142348    .002176    98.46   0.000     .2099499    .2185196
        290  |   .4642348    .002176   213.35   0.000     .4599499    .4685196
        291  |  -.0302891   .0008085   -37.46   0.000    -.0318811    -.028697
        297  |  -.1334245    .000413  -323.09   0.000    -.1342377   -.1326113
        300  |  -.0991624   .0002068  -479.43   0.000    -.0995697   -.0987551
        301  |   .0664252   .0009822    67.63   0.000     .0644912    .0683593
        304  |  -.0357652    .002176   -16.44   0.000    -.0400501   -.0314804
        305  |  -.1216715   .0002309  -527.03   0.000    -.1221262   -.1212169
        309  |  -.1518605   .0005258  -288.84   0.000    -.1528958   -.1508252
        312  |  -.1951303   .0018033  -108.21   0.000    -.1986814   -.1915793
        316  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        317  |  -.1313843   .0002116  -620.89   0.000     -.131801   -.1309676
        320  |  -.2830272   .0006837  -413.94   0.000    -.2843735   -.2816808
        322  |  -.0614938   .0001519  -404.78   0.000     -.061793   -.0611947
        398  |  -.1476599    .000618  -238.93   0.000    -.1488768   -.1464429
        399  |   .1345522   .0001863   722.15   0.000     .1341853    .1349191
        400  |  -.4052891   .0008085  -501.28   0.000    -.4068811    -.403697
        402  |  -.0417036   .0000693  -601.93   0.000    -.0418401   -.0415672
        403  |   .0969767   .0001542   628.78   0.000      .096673    .0972804
        405  |   -.113166   .0010572  -107.04   0.000    -.1152479   -.1110842
        407  |  -.0248129    .003793    -6.54   0.000    -.0322818   -.0173439
        408  |  -.3270033   .0025992  -125.81   0.000    -.3321216   -.3218851
        410  |  -.0411667   .0002233  -184.35   0.000    -.0416065    -.040727
        418  |  -.2816581   .0000624 -4514.89   0.000     -.281781   -.2815353
        422  |  -.0794181   .0004042  -196.50   0.000    -.0802139   -.0786222
        423  |  -.0284637   .0018033   -15.78   0.000    -.0320147   -.0249126
        425  |   .0994692   .0002575   386.25   0.000     .0989621    .0999763
        426  |    .037066   .0002553   145.20   0.000     .0365633    .0375686
        430  |  -.2024319    .002176   -93.03   0.000    -.2067168   -.1981471
        433  |   .4751871    .003793   125.28   0.000     .4677182    .4826561
        434  |   .0947109   .0008085   117.14   0.000     .0931189     .096303
        436  |   .2095266   .0003468   604.21   0.000     .2088437    .2102095
        440  |   .2975681    .002176   136.75   0.000     .2932832    .3018529
        441  |   .1702164   .0001278  1331.55   0.000     .1699646    .1704681
        444  |  -.1008272   .0003338  -302.07   0.000    -.1014845   -.1001699
        445  |   .0495034   .0000433  1144.50   0.000     .0494182    .0495885
        475  |   .1496799   .0001597   937.23   0.000     .1493654    .1499944
        478  |  -.5248129    .003793  -138.36   0.000    -.5322818   -.5173439
        481  |   .0756283   .0000212  3569.44   0.000     .0755865      .07567
        484  |  -.5248129    .003793  -138.36   0.000    -.5322818   -.5173439
        491  |  -.2745544   .0000411 -6685.57   0.000    -.2746352   -.2744735
        494  |  -.0309832   .0004302   -72.02   0.000    -.0318303   -.0301361
        498  |  -.1914795    .003793   -50.48   0.000    -.1989485   -.1840106
        499  |   .0460643   .0006052    76.12   0.000     .0448726    .0472561
        500  |   .0761658   .0003141   242.46   0.000     .0755472    .0767844
        503  |  -.1042608   .0000133 -7834.06   0.000     -.104287   -.1042346
        505  |  -.1313843   .0002116  -620.89   0.000     -.131801   -.1309676
        507  |   .0077512   .0005789    13.39   0.000     .0066112    .0088912
        508  |  -.1024999   .0003821  -268.22   0.000    -.1032525   -.1017474
        529  |  -.1460949   .0005789  -252.35   0.000    -.1472349   -.1449549
        531  |  -.0179355   .0003067   -58.48   0.000    -.0185394   -.0173316
        535  |   .1083803   .0002797   387.46   0.000     .1078295    .1089311
        536  |  -.1308367   .0005101  -256.52   0.000    -.1318411   -.1298323
        538  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        541  |  -.1723639   .0012349  -139.58   0.000    -.1747955   -.1699323
        543  |   .0302346   .0000732   413.06   0.000     .0300905    .0303788
        545  |  -.0993707   .0020876   -47.60   0.000    -.1034814   -.0952599
        560  |  -.0302891   .0008085   -37.46   0.000    -.0318811    -.028697
        562  |  -.1926965   .0031298   -61.57   0.000    -.1988595   -.1865335
        563  |   .0708062   .0014054    50.38   0.000     .0680387    .0735736
        564  |  -.0249128   .0002429  -102.55   0.000    -.0253912   -.0244345
        576  |  -.5248129    .003793  -138.36   0.000    -.5322818   -.5173439
        577  |   .0377526   .0000802   470.53   0.000     .0375946    .0379106
        578  |   .0476015   .0002326   204.69   0.000     .0471436    .0480594
        581  |   .0546697   .0000524  1043.26   0.000     .0545665    .0547729
        583  |  -.0302891   .0008085   -37.46   0.000    -.0318811    -.028697
        584  |   .0183419   .0000624   294.01   0.000      .018219    .0184647
        588  |   .0393141   .0001863   211.00   0.000     .0389472     .039681
        592  |   .0308545   .0004105    75.16   0.000     .0300461    .0316628
        593  |  -.1322364   .0001617  -817.90   0.000    -.1325547    -.131918
        595  |   .4642348    .002176   213.35   0.000     .4599499    .4685196
        598  |  -.1638855    .000291  -563.10   0.000    -.1644587   -.1633124
        599  |  -.5321145   .0001863 -2855.90   0.000    -.5324814   -.5317476
        601  |  -.1477799   .0003394  -435.45   0.000    -.1484482   -.1471116
        604  |   .0853685    .001039    82.16   0.000     .0833225    .0874146
        607  |  -.0145584   .0000148  -983.66   0.000    -.0145875   -.0145292
        608  |  -.0307652    .000549   -56.04   0.000    -.0318463   -.0296842
        609  |  -.0577399   .0002372  -243.41   0.000    -.0582071   -.0572728
        611  |  -.5321145   .0001863 -2855.90   0.000    -.5324814   -.5317476
        614  |  -.0467732    .000908   -51.51   0.000    -.0485612   -.0449852
        615  |  -.2766383   .0027981   -98.86   0.000    -.2821483   -.2711283
        620  |    .166699    .000833   200.13   0.000     .1650588    .1683393
        623  |  -.0764904   .0004015  -190.50   0.000     -.077281   -.0756997
        624  |   .0835962   .0009442    88.54   0.000      .081737    .0854554
        625  |  -.0316581   .0000624  -507.47   0.000     -.031781   -.0315353
        626  |  -.0317691   1.89e-06 -1.7e+04   0.000    -.0317728   -.0317654
        630  |   .4642348    .002176   213.35   0.000     .4599499    .4685196
        631  |  -.5321145   .0001863 -2855.90   0.000    -.5324814   -.5317476
        635  |   .0471824   .0002412   195.61   0.000     .0467074    .0476574
        636  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        638  |  -.0813183    .000033 -2463.47   0.000    -.0813833   -.0812533
        678  |  -.0462703   .0003563  -129.86   0.000    -.0469719   -.0455687
        680  |   .0129868    .000024   541.88   0.000     .0129397     .013034
        683  |   .2280575   .0003111   733.09   0.000     .2274449    .2286701
        684  |  -.1441383   .0006837  -210.81   0.000    -.1454846   -.1427919
        687  |   .0664252   .0009822    67.63   0.000     .0644912    .0683593
        689  |  -.5302891   .0008085  -655.89   0.000    -.5318811    -.528697
        691  |  -.0257825   .0000185 -1392.15   0.000    -.0258189    -.025746
        694  |  -.2191581   .0000624 -3513.03   0.000     -.219281   -.2190353
        697  |   -.150119   .0003821  -392.83   0.000    -.1508715   -.1493665
        698  |  -.0612791   .0002315  -264.68   0.000     -.061735   -.0608232
        704  |  -.5321145   .0001863 -2855.90   0.000    -.5324814   -.5317476
        707  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        710  |  -.4546297   .0001198 -3795.61   0.000    -.4548656   -.4543939
        729  |  -.0396243   .0004462   -88.80   0.000     -.040503   -.0387456
        732  |  -.1951303   .0018033  -108.21   0.000    -.1986814   -.1915793
        734  |  -.0212091   .0001958  -108.30   0.000    -.0215948   -.0208235
        738  |   .4642348    .002176   213.35   0.000     .4599499    .4685196
        740  |  -.0291938   .0014054   -20.77   0.000    -.0319613   -.0264264
        743  |  -.0438273   .0002326  -188.43   0.000    -.0442853   -.0433693
        746  |   .0865821   .0000531  1630.65   0.000     .0864776    .0866867
        747  |   .1135469   .0004769   238.10   0.000     .1126078     .114486
        748  |  -.0302891   .0008085   -37.46   0.000    -.0318811    -.028697
        749  |  -.1291938   .0014054   -91.93   0.000    -.1319613   -.1264264
        751  |  -.5248129    .003793  -138.36   0.000    -.5322818   -.5173439
        753  |   .0540146    .000093   580.51   0.000     .0538314    .0541978
        755  |   .0686157   .0002116   324.26   0.000      .068199    .0690324
        758  |  -.1456594   .0015128   -96.29   0.000    -.1486383   -.1426806
        759  |   .0909561   .0001211   751.16   0.000     .0907176    .0911945
        761  |   .0399102   .0001385   288.12   0.000     .0396374    .0401829
        762  |  -.0595209   .0003017  -197.28   0.000     -.060115   -.0589268
        765  |  -.0538486     .00017  -316.70   0.000    -.0541835   -.0535138
        768  |   .0317073   .0003394    93.43   0.000      .031039    .0323756
        777  |  -.0302891   .0008085   -37.46   0.000    -.0318811    -.028697
        778  |  -.1951303   .0018033  -108.21   0.000    -.1986814   -.1915793
        781  |   .0215222   .0001508   142.73   0.000     .0212252    .0218191
        783  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        785  |  -.1508746   .0013731  -109.88   0.000    -.1535786   -.1481707
        790  |  -.1469374   .0001198 -1226.75   0.000    -.1471733   -.1467015
        791  |  -.0302891   .0008085   -37.46   0.000    -.0318811    -.028697
        831  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        832  |  -.1951303   .0018033  -108.21   0.000    -.1986814   -.1915793
        833  |  -.5248129    .003793  -138.36   0.000    -.5322818   -.5173439
        834  |   .3030443   .0008085   374.82   0.000     .3014522    .3046363
        837  |   .0118797   .0001981    59.97   0.000     .0114896    .0122697
        845  |  -.0869399   .0002116  -410.85   0.000    -.0873565   -.0865232
        846  |  -.5302891   .0008085  -655.89   0.000    -.5318811    -.528697
        848  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        849  |   .1498386   .0002462   608.58   0.000     .1493538    .1503234
        850  |  -.0712736   .0005665  -125.81   0.000    -.0723892    -.070158
        851  |   -.060023   .0006329   -94.83   0.000    -.0612693   -.0587766
        853  |  -.0822833   .0003863  -213.00   0.000     -.083044   -.0815225
        854  |   .4697109   .0008085   580.97   0.000     .4681189     .471303
        858  |  -.0186996   .0002401   -77.88   0.000    -.0191724   -.0182268
        859  |  -.1987811   .0001863 -1066.87   0.000     -.199148   -.1984142
        886  |  -.4529447   .0010381  -436.33   0.000    -.4549889   -.4509006
        889  |  -.3056875   .0008694  -351.60   0.000    -.3073995   -.3039755
        892  |  -.0330272   .0006837   -48.30   0.000    -.0343735   -.0316808
        893  |  -.0248129    .003793    -6.54   0.000    -.0322818   -.0173439
        895  |  -.0350565   .0003227  -108.65   0.000    -.0356919   -.0344211
        905  |  -.0063407   .0000689   -92.04   0.000    -.0064764   -.0062051
        908  |  -.0734199   .0002228  -329.59   0.000    -.0738586   -.0729812
        915  |    .138203   .0018033    76.64   0.000      .134652     .141754
        918  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        921  |  -.5357652    .002176  -246.22   0.000    -.5400501   -.5314804
        924  |  -.0302891   .0008085   -37.46   0.000    -.0318811    -.028697
        925  |  -.1070568   .0000317 -3374.85   0.000    -.1071193   -.1069944
        927  |  -.0532968    .000504  -105.76   0.000    -.0542892   -.0523044
        931  |  -.1415647   .0004001  -353.82   0.000    -.1423526   -.1407769
        935  |   .1768944   .0007357   240.44   0.000     .1754457    .1783432
        936  |   .4642348    .002176   213.35   0.000     .4599499    .4685196
        946  |   .1320219   .0003776   349.64   0.000     .1312783    .1327654
        977  |  -.0416778   5.32e-06 -7840.19   0.000    -.0416883   -.0416674
        980  |  -.3217803    .000291 -1105.63   0.000    -.3223534   -.3212072
        981  |  -.0143695   .0000881  -163.08   0.000     -.014543    -.014196
        989  |  -.1311652    .000331  -396.29   0.000     -.131817   -.1305135
        992  |  -.1324795   .0003853  -343.85   0.000    -.1332382   -.1317208
             |
       _cons |   .5248129    .003793   138.36   0.000     .5173439    .5322818
------------------------------------------------------------------------------

. estadd local Controls "No"

added macro:
           e(Controls) : "No"

. estadd local Controls1 " "

added macro:
          e(Controls1) : " "

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "Yes"

added macro:
          e(Controls5) : "Yes"

. mean voted22 if e(sample)==1 & treatedf==0

Mean estimation                         Number of obs = 14,810

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .4943957   .0041085      .4863426    .5024488
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M4

. eststo M5:  reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool i.kunta19, clus
> ter(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     36,876
                                                F(13, 259)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1564
                                                Root MSE          =     .46095

                                (Std. err. adjusted for 260 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0110835   .0054718     2.03   0.044     .0003086    .0218584
     molincome |   .0268233   .0042144     6.36   0.000     .0185246    .0351221
        female |   .0094383   .0046116     2.05   0.042     .0003573    .0185193
           age |   .0088335   .0002696    32.77   0.000     .0083027    .0093643
       foreign |  -.2355494   .0087989   -26.77   0.000    -.2528758    -.218223
               |
       moses1d |
            1  |   .2072726   .0194241    10.67   0.000     .1690234    .2455218
            2  |   .0957322   .0168565     5.68   0.000      .062539    .1289254
            3  |   .1756451   .0115923    15.15   0.000      .152818    .1984722
            4  |   .0876613   .0113751     7.71   0.000     .0652619    .1100608
            5  |   .0063433   .0102614     0.62   0.537    -.0138631    .0265497
            6  |    .130984   .0161735     8.10   0.000     .0991358    .1628322
            7  |   .0127153   .0176139     0.72   0.471    -.0219694    .0474001
            9  |   .0641634   .0238794     2.69   0.008     .0171409    .1111859
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1462662   .0066043    22.15   0.000     .1332612    .1592713
               |
       kunta19 |
            9  |   .0625676   .0012446    50.27   0.000     .0601168    .0650185
           10  |  -.0730925   .0009426   -77.55   0.000    -.0749485   -.0712364
           16  |   .5300605   .0049197   107.74   0.000     .5203727    .5397482
           18  |   .5037269   .0077746    64.79   0.000     .4884175    .5190364
           19  |  -.1372044   .0025448   -53.92   0.000    -.1422156   -.1321932
           20  |  -.2137362   .0057578   -37.12   0.000    -.2250743   -.2023981
           47  |    .399176   .0067668    58.99   0.000      .385851     .412501
           49  |   .0392113   .0046286     8.47   0.000     .0300968    .0483259
           50  |   .3133012    .004567    68.60   0.000     .3043081    .3222943
           51  |   .0285117   .0016511    17.27   0.000     .0252603    .0317631
           61  |  -.0955396   .0079604   -12.00   0.000     -.111215   -.0798642
           69  |   .0485199   .0009481    51.18   0.000     .0466529    .0503869
           71  |   .1034113   .0013333    77.56   0.000     .1007859    .1060368
           72  |   .1532536   .0026298    58.27   0.000      .148075    .1584322
           74  |   .0451527   .0017296    26.11   0.000     .0417469    .0485585
           75  |   .2059563   .0110156    18.70   0.000     .1842647     .227648
           77  |   .0164502   .0009379    17.54   0.000     .0146032    .0182971
           78  |  -.2799712   .0074656   -37.50   0.000    -.2946723   -.2652701
           79  |  -.0531743   .0021521   -24.71   0.000    -.0574122   -.0489364
           81  |  -.2867837   .0091568   -31.32   0.000    -.3048149   -.2687525
           82  |   .5388013   .0074278    72.54   0.000     .5241747     .553428
           86  |  -.0972232   .0011412   -85.19   0.000    -.0994704   -.0949761
           90  |   .1296463   .0049839    26.01   0.000     .1198323    .1394604
           91  |  -.0262799   .0051138    -5.14   0.000    -.0363498     -.01621
           92  |  -.0426735   .0025831   -16.52   0.000    -.0477601    -.037587
           98  |   .1242373   .0044983    27.62   0.000     .1153794    .1330953
           99  |  -.2595451   .0061041   -42.52   0.000    -.2715651   -.2475251
          102  |   .1588554   .0074675    21.27   0.000     .1441507      .17356
          105  |   .7362716   .0070741   104.08   0.000     .7223416    .7502016
          106  |  -.1305135    .005585   -23.37   0.000    -.1415112   -.1195158
          108  |  -.0070745   .0016429    -4.31   0.000    -.0103097   -.0038393
          109  |  -.0994547   .0059365   -16.75   0.000    -.1111446   -.0877647
          111  |   .2753047   .0052669    52.27   0.000     .2649334     .285676
          139  |   .1489249   .0073158    20.36   0.000      .134519    .1633309
          140  |  -.1751467   .0055211   -31.72   0.000    -.1860186   -.1642747
          143  |  -.4342951   .0059259   -73.29   0.000    -.4459642    -.422626
          146  |  -.1599564   .0023289   -68.68   0.000    -.1645424   -.1553703
          148  |  -.0949701   .0027376   -34.69   0.000     -.100361   -.0895792
          151  |   .0895221   .0017138    52.24   0.000     .0861474    .0928968
          152  |   .6855906   .0071171    96.33   0.000     .6715758    .6996053
          153  |   .0436376   .0097098     4.49   0.000     .0245173    .0627579
          165  |  -.0239679   .0016643   -14.40   0.000    -.0272451   -.0206907
          167  |   -.020271   .0025857    -7.84   0.000    -.0253627   -.0151794
          169  |   .0509939   .0018699    27.27   0.000     .0473118    .0546761
          171  |  -.3890575   .0139457   -27.90   0.000    -.4165188   -.3615962
          176  |  -.3158967   .0108623   -29.08   0.000    -.3372864    -.294507
          177  |   .0754787    .001397    54.03   0.000     .0727277    .0782296
          178  |  -.5125657   .0094796   -54.07   0.000    -.5312325   -.4938989
          179  |   .0123223   .0028105     4.38   0.000     .0067879    .0178567
          181  |  -.2083272   .0059746   -34.87   0.000    -.2200922   -.1965622
          182  |   -.056318   .0018602   -30.27   0.000     -.059981   -.0526549
          186  |  -.0387888   .0043875    -8.84   0.000    -.0474285   -.0301491
          202  |  -.0300273   .0023044   -13.03   0.000     -.034565   -.0254895
          204  |   .4961129   .0070829    70.04   0.000     .4821654    .5100604
          205  |  -.0302658   .0047487    -6.37   0.000    -.0396167   -.0209148
          208  |    .044533   .0035577    12.52   0.000     .0375273    .0515386
          211  |  -.1048018   .0042937   -24.41   0.000    -.1132568   -.0963468
          213  |   .2598947   .0112944    23.01   0.000     .2376542    .2821353
          214  |  -.2987942   .0092554   -32.28   0.000    -.3170196   -.2805689
          216  |  -.0946571   .0035343   -26.78   0.000    -.1016167   -.0876974
          217  |   .0598195   .0013314    44.93   0.000     .0571978    .0624412
          218  |  -.2425297    .007284   -33.30   0.000    -.2568732   -.2281863
          224  |  -.0827373   .0023025   -35.93   0.000    -.0872714   -.0782033
          226  |  -.0372035   .0012488   -29.79   0.000    -.0396626   -.0347444
          230  |   .0507406   .0019504    26.02   0.000     .0468999    .0545813
          231  |  -.0546624   .0028904   -18.91   0.000    -.0603541   -.0489707
          232  |   -.040666   .0058672    -6.93   0.000    -.0522195   -.0291125
          233  |  -.0453291    .005801    -7.81   0.000    -.0567522   -.0339059
          235  |  -.4332928   .0077468   -55.93   0.000    -.4485475   -.4180381
          236  |   .0896798   .0014132    63.46   0.000      .086897    .0924626
          239  |   .1975782   .0027059    73.02   0.000     .1922498    .2029066
          240  |  -.0090377   .0022212    -4.07   0.000    -.0134116   -.0046639
          241  |  -.1349174   .0044276   -30.47   0.000    -.1436361   -.1261987
          244  |    .019368    .002244     8.63   0.000     .0149493    .0237867
          245  |  -.0268936   .0022646   -11.88   0.000    -.0313529   -.0224343
          249  |   .1459212   .0083727    17.43   0.000     .1294339    .1624085
          250  |  -.1811154    .008264   -21.92   0.000    -.1973887   -.1648421
          256  |  -.3103659   .0072316   -42.92   0.000    -.3246062   -.2961256
          257  |   -.006315   .0026968    -2.34   0.020    -.0116256   -.0010045
          260  |  -.2443528   .0049973   -48.90   0.000    -.2541933   -.2345122
          261  |  -.0364925   .0069362    -5.26   0.000    -.0501511    -.022834
          263  |   -.285104   .0124373   -22.92   0.000     -.309595    -.260613
          265  |  -.0546889   .0026489   -20.65   0.000     -.059905   -.0494728
          271  |   .1194775   .0054082    22.09   0.000      .108828    .1301271
          272  |   .0961731   .0067461    14.26   0.000     .0828889    .1094573
          273  |   .1693673    .013009    13.02   0.000     .1437505    .1949841
          275  |  -.4228363   .0077419   -54.62   0.000    -.4380814   -.4075912
          276  |   -.069434   .0017816   -38.97   0.000    -.0729422   -.0659258
          284  |  -.4370792   .0158582   -27.56   0.000    -.4683066   -.4058517
          285  |  -.0713746   .0063662   -11.21   0.000    -.0839107   -.0588384
          286  |   .0917712   .0072567    12.65   0.000     .0774816    .1060608
          287  |   .0797766   .0017252    46.24   0.000     .0763794    .0831739
          288  |   .3041144   .0059913    50.76   0.000     .2923165    .3159122
          290  |   .5421037   .0096869    55.96   0.000     .5230285    .5611789
          291  |   .1247858   .0119032    10.48   0.000     .1013463    .1482252
          297  |   .0115116   .0052875     2.18   0.030     .0010998    .0219235
          300  |   -.094221   .0010415   -90.47   0.000    -.0962719   -.0921701
          301  |   .2040855   .0060285    33.85   0.000     .1922143    .2159566
          304  |   .0397406   .0064541     6.16   0.000     .0270314    .0524498
          305  |  -.0884956   .0011589   -76.36   0.000    -.0907778   -.0862135
          309  |  -.0861848   .0018731   -46.01   0.000    -.0898733   -.0824962
          312  |   .1577656   .0052627    29.98   0.000     .1474025    .1681286
          316  |   -.241628   .0196182   -12.32   0.000    -.2802595   -.2029966
          317  |   .1835872    .008885    20.66   0.000     .1660911    .2010833
          320  |  -.1312209   .0101005   -12.99   0.000    -.1511104   -.1113313
          322  |  -.0230133   .0015618   -14.74   0.000    -.0260887   -.0199379
          398  |  -.0112397   .0048693    -2.31   0.022    -.0208282   -.0016512
          399  |    .373617   .0090353    41.35   0.000      .355825     .391409
          400  |  -.1615063   .0051746   -31.21   0.000    -.1716961   -.1513166
          402  |   .0009792   .0011295     0.87   0.387    -.0012449    .0032032
          403  |   .0791392   .0013647    57.99   0.000     .0764519    .0818264
          405  |    .023541   .0057077     4.12   0.000     .0123015    .0347805
          407  |   .2464427   .0106925    23.05   0.000     .2253874     .267498
          408  |  -.1189822   .0081487   -14.60   0.000    -.1350283    -.102936
          410  |  -.0056717    .001763    -3.22   0.001    -.0091434      -.0022
          418  |  -.0838474   .0059024   -14.21   0.000    -.0954703   -.0722245
          422  |   -.018651    .001731   -10.77   0.000    -.0220596   -.0152423
          423  |   .1982907   .0061669    32.15   0.000     .1861471    .2104344
          425  |   .1040824   .0014354    72.51   0.000     .1012559     .106909
          426  |   .0571874    .001043    54.83   0.000     .0551336    .0592411
          430  |  -.0073152   .0095046    -0.77   0.442    -.0260313     .011401
          433  |   .3817219   .0091711    41.62   0.000     .3636626    .3997813
          434  |   .1805233   .0035644    50.65   0.000     .1735045    .1875422
          436  |   .2542437   .0019154   132.74   0.000     .2504719    .2580154
          440  |   .3922638   .0041355    94.85   0.000     .3841203    .4004072
          441  |   .1545053   .0018824    82.08   0.000     .1507986     .158212
          444  |  -.0995222    .001738   -57.26   0.000    -.1029446   -.0960997
          445  |   .0371361   .0019171    19.37   0.000      .033361    .0409113
          475  |   .1797059   .0010345   173.71   0.000     .1776688    .1817431
          478  |  -.4736833   .0156709   -30.23   0.000    -.5045418   -.4428248
          481  |   .0403125   .0017419    23.14   0.000     .0368824    .0437426
          484  |   -.452289   .0091221   -49.58   0.000    -.4702519   -.4343262
          491  |  -.1591456   .0053134   -29.95   0.000    -.1696086   -.1486826
          494  |   .0184635   .0016838    10.97   0.000     .0151477    .0217792
          498  |   .0781147   .0131784     5.93   0.000     .0521643    .1040651
          499  |    .109618   .0026789    40.92   0.000     .1043429    .1148932
          500  |   .0391607   .0022307    17.56   0.000     .0347681    .0435533
          503  |  -.1062455   .0015123   -70.25   0.000    -.1092235   -.1032675
          505  |  -.0382662   .0031817   -12.03   0.000    -.0445316   -.0320008
          507  |  -.0204193   .0011778   -17.34   0.000    -.0227385   -.0181001
          508  |   .0515885   .0034463    14.97   0.000     .0448022    .0583747
          529  |  -.0198978   .0036818    -5.40   0.000    -.0271478   -.0126478
          531  |  -.0207701   .0016373   -12.69   0.000    -.0239942    -.017546
          535  |   .1403864   .0012156   115.49   0.000     .1379927      .14278
          536  |  -.0112254   .0049714    -2.26   0.025    -.0210148   -.0014359
          538  |  -.2781461   .0161924   -17.18   0.000    -.3100316   -.2462606
          541  |   -.086159   .0044896   -19.19   0.000    -.0949998   -.0773181
          543  |   .0167634   .0020001     8.38   0.000     .0128249    .0207019
          545  |  -.0093007    .004591    -2.03   0.044    -.0183411   -.0002603
          560  |   .1397063   .0065333    21.38   0.000      .126841    .1525715
          562  |  -.0877196   .0059257   -14.80   0.000    -.0993882   -.0760509
          563  |   .1012046   .0038825    26.07   0.000     .0935593    .1088498
          564  |   .0151974   .0026804     5.67   0.000     .0099192    .0204757
          576  |   -.474616   .0147749   -32.12   0.000    -.5037103   -.4455217
          577  |   .0126831   .0019816     6.40   0.000      .008781    .0165853
          578  |   .1014747   .0013122    77.33   0.000     .0988906    .1040587
          581  |   .0692081   .0012875    53.75   0.000     .0666728    .0717434
          583  |    .011325   .0027322     4.14   0.000     .0059448    .0167052
          584  |   .0679321   .0013536    50.19   0.000     .0652666    .0705976
          588  |   .0595467   .0021717    27.42   0.000     .0552703    .0638231
          592  |   .0488436   .0016396    29.79   0.000     .0456149    .0520724
          593  |  -.1203578   .0013871   -86.77   0.000    -.1230893   -.1176263
          595  |    .511492   .0071772    71.27   0.000      .497359     .525625
          598  |  -.0435822   .0038584   -11.30   0.000      -.05118   -.0359843
          599  |  -.2374904   .0047565   -49.93   0.000    -.2468568    -.228124
          601  |  -.1739791   .0052993   -32.83   0.000    -.1844143   -.1635439
          604  |   .1620374   .0051161    31.67   0.000      .151963    .1721118
          607  |  -.0231412   .0023992    -9.65   0.000    -.0278657   -.0184168
          608  |  -.0280647   .0025465   -11.02   0.000    -.0330791   -.0230503
          609  |  -.0315118   .0020909   -15.07   0.000    -.0356292   -.0273944
          611  |  -.4109756   .0038675  -106.26   0.000    -.4185914   -.4033597
          614  |   -.016333   .0022013    -7.42   0.000    -.0206676   -.0119983
          615  |  -.1961378   .0052293   -37.51   0.000    -.2064351   -.1858405
          620  |   .1864485   .0020302    91.84   0.000     .1824508    .1904463
          623  |  -.1191912   .0021421   -55.64   0.000    -.1234094    -.114973
          624  |   .0582252   .0024666    23.61   0.000      .053368    .0630824
          625  |   .0191263   .0032944     5.81   0.000     .0126391    .0256135
          626  |   .0136064   .0014337     9.49   0.000     .0107832    .0164296
          630  |   .7135213   .0069032   103.36   0.000     .6999278    .7271149
          631  |  -.3005537   .0055811   -53.85   0.000    -.3115437   -.2895637
          635  |   .0521975   .0014036    37.19   0.000     .0494337    .0549614
          636  |  -.4175235   .0081194   -51.42   0.000    -.4335118   -.4015351
          638  |  -.0766174   .0020973   -36.53   0.000    -.0807474   -.0724873
          678  |   .1005676   .0057986    17.34   0.000     .0891493    .1119859
          680  |   .0157214   .0022654     6.94   0.000     .0112604    .0201824
          683  |   .2337874   .0019713   118.59   0.000     .2299055    .2376692
          684  |  -.0200421   .0052017    -3.85   0.000    -.0302851   -.0097991
          687  |    .079068   .0039526    20.00   0.000     .0712846    .0868513
          689  |  -.3318414   .0062022   -53.50   0.000    -.3440545   -.3196284
          691  |   -.010971   .0017482    -6.28   0.000    -.0144135   -.0075285
          694  |   .0137515   .0060325     2.28   0.023     .0018725    .0256305
          697  |  -.1599062   .0019321   -82.76   0.000    -.1637109   -.1561016
          698  |  -.0139524   .0023523    -5.93   0.000    -.0185845   -.0093204
          704  |  -.3539961   .0112765   -31.39   0.000    -.3762015   -.3317907
          707  |  -.3410332   .0056133   -60.75   0.000    -.3520868   -.3299796
          710  |  -.3131788   .0029399  -106.53   0.000    -.3189679   -.3073897
          729  |  -.0041346   .0013075    -3.16   0.002    -.0067094   -.0015599
          732  |  -.1723468   .0034445   -50.03   0.000    -.1791297   -.1655639
          734  |  -.0227795   .0010038   -22.69   0.000    -.0247561   -.0208029
          738  |   .4984701   .0051581    96.64   0.000     .4883129    .5086273
          740  |    .107596    .006929    15.53   0.000     .0939516    .1212404
          743  |   .0360455   .0044433     8.11   0.000     .0272959     .044795
          746  |   .1374668   .0013142   104.60   0.000      .134879    .1400547
          747  |   .0733985   .0023136    31.72   0.000     .0688426    .0779544
          748  |   .2893647   .0118047    24.51   0.000     .2661193    .3126101
          749  |  -.0703505   .0071564    -9.83   0.000    -.0844427   -.0562583
          751  |  -.4203627   .0078373   -53.64   0.000    -.4357957   -.4049297
          753  |   .0250798   .0021109    11.88   0.000      .020923    .0292365
          755  |    .117762   .0043795    26.89   0.000     .1091381    .1263859
          758  |  -.1208897   .0028959   -41.75   0.000    -.1265922   -.1151873
          759  |   .1230124   .0014952    82.27   0.000     .1200681    .1259567
          761  |   .0276791   .0009123    30.34   0.000     .0258827    .0294755
          762  |   -.060168   .0012412   -48.48   0.000    -.0626122   -.0577239
          765  |  -.0647717   .0010673   -60.69   0.000    -.0668733   -.0626701
          768  |   .0253607   .0018914    13.41   0.000     .0216361    .0290852
          777  |   .0637911   .0080231     7.95   0.000     .0479923    .0795899
          778  |    .011739   .0078169     1.50   0.134    -.0036537    .0271318
          781  |   .0497457   .0023804    20.90   0.000     .0450583    .0544331
          783  |  -.3702737   .0096498   -38.37   0.000    -.3892757   -.3512717
          785  |  -.1257948   .0025156   -50.01   0.000    -.1307485   -.1208411
          790  |   .0086935   .0049987     1.74   0.083    -.0011498    .0185367
          791  |   .1775935   .0056642    31.35   0.000     .1664397    .1887472
          831  |  -.3101309   .0072043   -43.05   0.000    -.3243175   -.2959444
          832  |   .0168334    .005262     3.20   0.002     .0064716    .0271951
          833  |  -.4444138   .0091291   -48.68   0.000    -.4623905   -.4264371
          834  |   .2672906   .0032629    81.92   0.000     .2608653    .2737158
          837  |   .0443138   .0028943    15.31   0.000     .0386145    .0500132
          845  |  -.0605637   .0020069   -30.18   0.000    -.0645156   -.0566118
          846  |  -.3545363   .0061531   -57.62   0.000    -.3666528   -.3424198
          848  |  -.1883028   .0119557   -15.75   0.000    -.2118456     -.16476
          849  |    .195494   .0013816   141.50   0.000     .1927735    .1982145
          850  |  -.1136318    .003363   -33.79   0.000    -.1202541   -.1070095
          851  |   .0209856   .0037383     5.61   0.000     .0136242     .028347
          853  |   .0527962   .0051492    10.25   0.000     .0426564    .0629359
          854  |   .7091552   .0110992    63.89   0.000     .6872991    .7310113
          858  |   .0869303   .0037268    23.33   0.000     .0795917    .0942689
          859  |   .0075205   .0053147     1.42   0.158    -.0029451    .0179861
          886  |  -.2085683   .0050082   -41.65   0.000    -.2184303   -.1987064
          889  |   -.238269   .0022548  -105.67   0.000     -.242709   -.2338289
          892  |   .1608593   .0067423    23.86   0.000     .1475826    .1741361
          893  |   .1953733   .0069551    28.09   0.000     .1816775    .2090691
          895  |   .0028413   .0013323     2.13   0.034     .0002177    .0054649
          905  |   .0037922   .0022359     1.70   0.091    -.0006106    .0081951
          908  |  -.0431058   .0021056   -20.47   0.000    -.0472521   -.0389595
          915  |   .3508495   .0069965    50.15   0.000     .3370723    .3646268
          918  |  -.4523531    .009756   -46.37   0.000    -.4715643   -.4331419
          921  |  -.4531323   .0072308   -62.67   0.000    -.4673708   -.4388937
          924  |   .1832794   .0065543    27.96   0.000     .1703728     .196186
          925  |  -.1090624   .0019987   -54.57   0.000    -.1129981   -.1051267
          927  |  -.0705115   .0019836   -35.55   0.000    -.0744176   -.0666054
          931  |   -.109883   .0015062   -72.95   0.000    -.1128489   -.1069171
          935  |   .1723322   .0020866    82.59   0.000     .1682234    .1764411
          936  |   .5245886   .0073658    71.22   0.000     .5100842     .539093
          946  |   .1508822   .0006321   238.68   0.000     .1496374     .152127
          977  |    .015183   .0017528     8.66   0.000     .0117314    .0186346
          980  |  -.1146215   .0070889   -16.17   0.000    -.1285807   -.1006623
          981  |  -.0377894   .0025981   -14.54   0.000    -.0429055   -.0326732
          989  |  -.1101477   .0010354  -106.38   0.000    -.1121865   -.1081088
          992  |  -.0082777   .0040705    -2.03   0.043    -.0162933   -.0002621
               |
         _cons |  -.2739117    .047045    -5.82   0.000    -.3665511   -.1812722
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "Yes"

added macro:
          e(Controls5) : "Yes"

. mean voted22 if e(sample)==1 & treatedf==0

Mean estimation                         Number of obs = 14,662

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |    .495976   .0041293      .4878821    .5040699
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M5

. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local numbers "& (1) & (2) & (3) & (4) & (5) \\ \hline"

. local emptyrow "& & & & &  \\ "

. local line "& & & & & \hline \\ "

. 
. local dstars " "

. local sestars " "

. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("Controls Controls" "Controls1 \phantom{controls}"  "Controls2 \phantom{controls}" "Controls3 \phantom{c
> ontrols}" "Controls4 \phantom{controls}"  "Controls5 Municipality FE" "umean Untreated $\bar{Y}$" "N Observation
> s") ///
> sfmt(%9.3f %9.3f %9.3f %9.3f  %9.3f  %9.3f  %9.3f %9.0fc) mlabels(none) nonumbers posthead("`header'" "`emptyrow
> '" "`numbers'" "`emptyrow'") ///
> refcat(treatedf "", nolabel below) title("Spillovers - Average Treatment Effect") nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

Spillovers - Average Treatment Effect
----------------------------------------------------------------------------------------------------
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & & &  \\ 
& (1) & (2) & (3) & (4) & (5) \\ \hline
& & & & &  \\ 
Treated in HH               0.014**         0.014**         0.013**         0.011*          0.011** 
                          (0.006)         (0.005)         (0.006)         (0.006)         (0.005)   

                                                                                                    
----------------------------------------------------------------------------------------------------
Controls                       No    Female, Age, Immigrant,    Female, Age, Immigrant,              No    Female,
>  Age, Immigrant,   
\phantom{controls}                      Ln Income      Ln Income,                      Ln Income,   
\phantom{controls}                                   SES Background,                    SES Background,   
\phantom{controls}                                   Educational Background,                    Educational Backgr
> ound,   
\phantom{controls}                                   First Time Eligble to Vote                    First Time Elig
> ble to Vote   
Municipality FE                No              No              No             Yes             Yes   
Untreated $\bar{Y}$         0.494           0.496           0.496           0.494           0.496   
Observations               37,207          36,876          36,876          37,207          36,876   
----------------------------------------------------------------------------------------------------

. 
. *************************************************
. *****TABLE A2
. *************************************************
. 
. eststo clear

. eststo M1: logit voted22 treatedf, cluster(kunta19)

Iteration 0:   log pseudolikelihood = -25789.427  
Iteration 1:   log pseudolikelihood = -25786.119  
Iteration 2:   log pseudolikelihood = -25786.119  

Logistic regression                                     Number of obs = 37,207
                                                        Wald chi2(1)  =   4.77
                                                        Prob > chi2   = 0.0290
Log pseudolikelihood = -25786.119                       Pseudo R2     = 0.0001

                              (Std. err. adjusted for 260 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
    treatedf |   .0544788   .0249485     2.18   0.029     .0055806     .103377
       _cons |  -.0224182   .0300471    -0.75   0.456    -.0813095     .036473
------------------------------------------------------------------------------

. estadd local Controls "No"

added macro:
           e(Controls) : "No"

. estadd local Controls1 " "

added macro:
          e(Controls1) : " "

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedf==0

Mean estimation                         Number of obs = 14,810

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .4943957   .0041085      .4863426    .5024488
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M1

. eststo M2: logit voted22 treatedf molincome female age foreign, cluster(kunta19)

Iteration 0:   log pseudolikelihood = -25559.242  
Iteration 1:   log pseudolikelihood = -23737.286  
Iteration 2:   log pseudolikelihood = -23727.768  
Iteration 3:   log pseudolikelihood =  -23727.75  
Iteration 4:   log pseudolikelihood =  -23727.75  

Logistic regression                                     Number of obs = 36,876
                                                        Wald chi2(5)  = 762.16
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -23727.75                        Pseudo R2     = 0.0717

                              (Std. err. adjusted for 260 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
    treatedf |   .0604097   .0235379     2.57   0.010     .0142762    .1065432
   molincome |   .3100653   .0255008    12.16   0.000     .2600847     .360046
      female |   .2206481   .0170437    12.95   0.000     .1872431    .2540531
         age |   .0346855   .0013771    25.19   0.000     .0319863    .0373846
     foreign |  -1.572426   .1101737   -14.27   0.000    -1.788363    -1.35649
       _cons |  -4.692408   .3266374   -14.37   0.000    -5.332605    -4.05221
------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income"

added macro:
          e(Controls1) : "Ln Income"

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedf==0

Mean estimation                         Number of obs = 14,662

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |    .495976   .0041293      .4878821    .5040699
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M2

. eststo M3:  logit voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(kunt
> a19)

note: firstvote omitted because of collinearity.
Iteration 0:   log pseudolikelihood = -25559.242  
Iteration 1:   log pseudolikelihood = -22792.452  
Iteration 2:   log pseudolikelihood = -22785.882  
Iteration 3:   log pseudolikelihood = -22785.859  
Iteration 4:   log pseudolikelihood = -22785.859  

Logistic regression                                    Number of obs =  36,876
                                                       Wald chi2(14) = 4759.74
                                                       Prob > chi2   =  0.0000
Log pseudolikelihood = -22785.859                      Pseudo R2     =  0.1085

                                (Std. err. adjusted for 260 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0604331   .0256158     2.36   0.018     .0102271    .1106391
     molincome |   .1208084   .0198359     6.09   0.000     .0819308    .1596861
        female |   .0444151    .020694     2.15   0.032     .0038555    .0849746
           age |   .0387178    .001255    30.85   0.000      .036258    .0411775
       foreign |  -1.350945   .0978338   -13.81   0.000    -1.542696   -1.159194
               |
       moses1d |
            1  |   1.020691   .1017294    10.03   0.000      .821305    1.220077
            2  |   .4411042   .0754919     5.84   0.000     .2931428    .5890655
            3  |   .8149257   .0538667    15.13   0.000      .709349    .9205025
            4  |   .3942506   .0553663     7.12   0.000     .2857346    .5027666
            5  |    .024658   .0507163     0.49   0.627    -.0747443    .1240602
            6  |   .6273639   .0721302     8.70   0.000     .4859914    .7687365
            7  |   .0691004   .0809043     0.85   0.393    -.0894692    .2276699
            9  |   .2879623   .1137602     2.53   0.011     .0649965    .5109282
               |
     firstvote |          0  (omitted)
1.mohighschool |   .6507138   .0356208    18.27   0.000     .5808983    .7205293
         _cons |  -3.457187   .2421265   -14.28   0.000    -3.931746   -2.982628
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedf==0

Mean estimation                         Number of obs = 14,662

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |    .495976   .0041293      .4878821    .5040699
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M3

. eststo M4:  logit voted22 treatedf i.kunta19, cluster(kunta19)

note: 16.kunta19 != 0 predicts success perfectly;
      16.kunta19 omitted and 1 obs not used.

note: 18.kunta19 != 0 predicts success perfectly;
      18.kunta19 omitted and 1 obs not used.

note: 47.kunta19 != 0 predicts success perfectly;
      47.kunta19 omitted and 1 obs not used.

note: 78.kunta19 != 0 predicts failure perfectly;
      78.kunta19 omitted and 1 obs not used.

note: 81.kunta19 != 0 predicts failure perfectly;
      81.kunta19 omitted and 2 obs not used.

note: 82.kunta19 != 0 predicts success perfectly;
      82.kunta19 omitted and 1 obs not used.

note: 99.kunta19 != 0 predicts failure perfectly;
      99.kunta19 omitted and 2 obs not used.

note: 105.kunta19 != 0 predicts success perfectly;
      105.kunta19 omitted and 1 obs not used.

note: 143.kunta19 != 0 predicts failure perfectly;
      143.kunta19 omitted and 2 obs not used.

note: 152.kunta19 != 0 predicts success perfectly;
      152.kunta19 omitted and 1 obs not used.

note: 171.kunta19 != 0 predicts failure perfectly;
      171.kunta19 omitted and 2 obs not used.

note: 176.kunta19 != 0 predicts failure perfectly;
      176.kunta19 omitted and 2 obs not used.

note: 178.kunta19 != 0 predicts failure perfectly;
      178.kunta19 omitted and 1 obs not used.

note: 181.kunta19 != 0 predicts failure perfectly;
      181.kunta19 omitted and 3 obs not used.

note: 204.kunta19 != 0 predicts success perfectly;
      204.kunta19 omitted and 2 obs not used.

note: 214.kunta19 != 0 predicts failure perfectly;
      214.kunta19 omitted and 4 obs not used.

note: 218.kunta19 != 0 predicts failure perfectly;
      218.kunta19 omitted and 1 obs not used.

note: 235.kunta19 != 0 predicts failure perfectly;
      235.kunta19 omitted and 1 obs not used.

note: 250.kunta19 != 0 predicts failure perfectly;
      250.kunta19 omitted and 1 obs not used.

note: 256.kunta19 != 0 predicts failure perfectly;
      256.kunta19 omitted and 1 obs not used.

note: 263.kunta19 != 0 predicts failure perfectly;
      263.kunta19 omitted and 2 obs not used.

note: 275.kunta19 != 0 predicts failure perfectly;
      275.kunta19 omitted and 1 obs not used.

note: 284.kunta19 != 0 predicts failure perfectly;
      284.kunta19 omitted and 1 obs not used.

note: 290.kunta19 != 0 predicts success perfectly;
      290.kunta19 omitted and 1 obs not used.

note: 316.kunta19 != 0 predicts failure perfectly;
      316.kunta19 omitted and 1 obs not used.

note: 433.kunta19 != 0 predicts success perfectly;
      433.kunta19 omitted and 2 obs not used.

note: 478.kunta19 != 0 predicts failure perfectly;
      478.kunta19 omitted and 1 obs not used.

note: 484.kunta19 != 0 predicts failure perfectly;
      484.kunta19 omitted and 1 obs not used.

note: 538.kunta19 != 0 predicts failure perfectly;
      538.kunta19 omitted and 1 obs not used.

note: 576.kunta19 != 0 predicts failure perfectly;
      576.kunta19 omitted and 1 obs not used.

note: 595.kunta19 != 0 predicts success perfectly;
      595.kunta19 omitted and 2 obs not used.

note: 599.kunta19 != 0 predicts failure perfectly;
      599.kunta19 omitted and 3 obs not used.

note: 611.kunta19 != 0 predicts failure perfectly;
      611.kunta19 omitted and 3 obs not used.

note: 630.kunta19 != 0 predicts success perfectly;
      630.kunta19 omitted and 1 obs not used.

note: 631.kunta19 != 0 predicts failure perfectly;
      631.kunta19 omitted and 3 obs not used.

note: 636.kunta19 != 0 predicts failure perfectly;
      636.kunta19 omitted and 2 obs not used.

note: 689.kunta19 != 0 predicts failure perfectly;
      689.kunta19 omitted and 2 obs not used.

note: 704.kunta19 != 0 predicts failure perfectly;
      704.kunta19 omitted and 3 obs not used.

note: 707.kunta19 != 0 predicts failure perfectly;
      707.kunta19 omitted and 4 obs not used.

note: 738.kunta19 != 0 predicts success perfectly;
      738.kunta19 omitted and 1 obs not used.

note: 751.kunta19 != 0 predicts failure perfectly;
      751.kunta19 omitted and 1 obs not used.

note: 783.kunta19 != 0 predicts failure perfectly;
      783.kunta19 omitted and 1 obs not used.

note: 831.kunta19 != 0 predicts failure perfectly;
      831.kunta19 omitted and 1 obs not used.

note: 833.kunta19 != 0 predicts failure perfectly;
      833.kunta19 omitted and 1 obs not used.

note: 846.kunta19 != 0 predicts failure perfectly;
      846.kunta19 omitted and 2 obs not used.

note: 848.kunta19 != 0 predicts failure perfectly;
      848.kunta19 omitted and 3 obs not used.

note: 854.kunta19 != 0 predicts success perfectly;
      854.kunta19 omitted and 2 obs not used.

note: 918.kunta19 != 0 predicts failure perfectly;
      918.kunta19 omitted and 1 obs not used.

note: 921.kunta19 != 0 predicts failure perfectly;
      921.kunta19 omitted and 1 obs not used.

note: 936.kunta19 != 0 predicts success perfectly;
      936.kunta19 omitted and 1 obs not used.

Iteration 0:   log pseudolikelihood = -25733.019  
Iteration 1:   log pseudolikelihood = -25384.321  
Iteration 2:   log pseudolikelihood = -25382.622  
Iteration 3:   log pseudolikelihood = -25382.602  
Iteration 4:   log pseudolikelihood = -25382.602  

Logistic regression                                     Number of obs = 37,126
                                                        Wald chi2(2)  =      .
                                                        Prob > chi2   =      .
Log pseudolikelihood = -25382.602                       Pseudo R2     = 0.0136

                              (Std. err. adjusted for 210 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
    treatedf |   .0446448   .0243087     1.84   0.066    -.0029994    .0922889
             |
     kunta19 |
          9  |   .2365674   .0005181   456.62   0.000     .2355519    .2375828
         10  |  -.3796244   .0017812  -213.13   0.000    -.3831155   -.3761333
         16  |          0  (empty)
         18  |          0  (empty)
         19  |   -.541762   .0022569  -240.04   0.000    -.5461855   -.5373384
         20  |  -1.909974   .0048237  -395.96   0.000    -1.919428    -1.90052
         47  |          0  (empty)
         49  |  -.5777154   .0003277 -1763.09   0.000    -.5783577   -.5770732
         50  |   .2708742   .0039815    68.03   0.000     .2630706    .2786777
         51  |   .1093711   .0004512   242.41   0.000     .1084868    .1102554
         61  |  -1.096075   .0064784  -169.19   0.000    -1.108772   -1.083377
         69  |   .0688698   .0010805    63.74   0.000      .066752    .0709876
         71  |   .3294983   .0021068   156.40   0.000     .3253691    .3336274
         72  |   .4602451   .0001247  3692.15   0.000     .4600007    .4604894
         74  |   .1794535   .0007379   243.19   0.000     .1780072    .1808998
         75  |    .549596   .0088769    61.91   0.000     .5321976    .5669944
         77  |   .0355614   .0013534    26.28   0.000     .0329088    .0382141
         78  |          0  (empty)
         79  |  -.2956748   .0011857  -249.37   0.000    -.2979987   -.2933509
         81  |          0  (empty)
         82  |          0  (empty)
         86  |   -.240087   .0005941  -404.14   0.000    -.2412513   -.2389226
         90  |    .549596   .0088769    61.91   0.000     .5321976    .5669944
         91  |  -.6799237   .0000973 -6986.79   0.000    -.6801144   -.6797329
         92  |  -.3077728    .001027  -299.69   0.000    -.3097857     -.30576
         98  |  -.1212288   .0032774   -36.99   0.000    -.1276524   -.1148052
         99  |          0  (empty)
        102  |    .549596   .0088769    61.91   0.000     .5321976    .5669944
        105  |          0  (empty)
        106  |  -1.224425   .0007162 -1709.67   0.000    -1.225829   -1.223022
        108  |  -.0300413   .0010386   -28.93   0.000    -.0320768   -.0280057
        109  |  -.8510871   .0037905  -224.53   0.000    -.8585164   -.8436579
        111  |   .9663156   .0026983   358.12   0.000      .961027    .9716041
        139  |  -.3468801   .0018968  -182.87   0.000    -.3505979   -.3431624
        140  |  -1.849292   .0067505  -273.95   0.000    -1.862523   -1.836061
        143  |          0  (empty)
        146  |  -.8668166   8.70e-06 -1.0e+05   0.000    -.8668336   -.8667995
        148  |  -.3137155   .0022679  -138.33   0.000    -.3181605   -.3092705
        151  |   .3553884   .0004646   764.99   0.000     .3544778    .3562989
        152  |          0  (empty)
        153  |  -.7920536   .0154318   -51.33   0.000    -.8222993   -.7618079
        165  |  -.0774858   .0013691   -56.59   0.000    -.0801692   -.0748023
        167  |   -.290254   .0002464 -1177.98   0.000     -.290737   -.2897711
        169  |   .1302963   .0004649   280.27   0.000     .1293852    .1312075
        171  |          0  (empty)
        176  |          0  (empty)
        177  |   .2365674   .0005181   456.62   0.000     .2355519    .2375828
        178  |          0  (empty)
        179  |   -.112309   .0007931  -141.61   0.000    -.1138634   -.1107546
        181  |          0  (empty)
        182  |  -.2883869   .0019977  -144.36   0.000    -.2923023   -.2844714
        186  |  -.5683057   .0059216   -95.97   0.000    -.5799118   -.5566995
        202  |  -.0299014   .0004608   -64.90   0.000    -.0308045   -.0289984
        204  |          0  (empty)
        205  |  -.8101479   .0055363  -146.33   0.000    -.8209988   -.7992969
        208  |  -.4121349   .0015027  -274.26   0.000    -.4150802   -.4091896
        211  |  -.8654141   .0020056  -431.49   0.000     -.869345   -.8614831
        213  |    .549596   .0088769    61.91   0.000     .5321976    .5669944
        214  |          0  (empty)
        216  |  -.6321167   .0032096  -196.95   0.000    -.6384074    -.625826
        217  |   .0798285   .0002643   302.00   0.000     .0793104    .0803466
        218  |          0  (empty)
        224  |  -.4028543   .0023516  -171.31   0.000    -.4074634   -.3982452
        226  |   .0120133   .0011304    10.63   0.000     .0097977    .0142289
        230  |   .1768279   .0018397    96.12   0.000      .173222    .1804337
        231  |  -.2176139   .0026856   -81.03   0.000    -.2228776   -.2123502
        232  |   -.821891    .000855  -961.26   0.000    -.8235667   -.8202152
        233  |  -.5222769   .0056567   -92.33   0.000    -.5333639     -.51119
        235  |          0  (empty)
        236  |   .2085841   .0003285   634.98   0.000     .2079402    .2092279
        239  |   .9061575    .002125   426.43   0.000     .9019926    .9103225
        240  |  -.2569426   .0026242   -97.91   0.000    -.2620858   -.2517993
        241  |   -.821891    .000855  -961.26   0.000    -.8235667   -.8202152
        244  |   .0320666   .0001457   220.14   0.000     .0317811    .0323521
        245  |  -.1585643   .0009309  -170.34   0.000    -.1603888   -.1567398
        249  |  -.5312065   .0007939  -669.07   0.000    -.5327626   -.5296504
        250  |          0  (empty)
        256  |          0  (empty)
        257  |   .1883337   .0002263   832.09   0.000     .1878901    .1887774
        260  |  -1.494225   .0104658  -142.77   0.000    -1.514738   -1.473713
        261  |   -.814459    .003187  -255.56   0.000    -.8207054   -.8082127
        263  |          0  (empty)
        265  |  -.5998674   .0014515  -413.27   0.000    -.6027123   -.5970226
        271  |  -.5490163   .0088769   -61.85   0.000    -.5664147   -.5316178
        272  |  -.3830957   .0010617  -360.83   0.000    -.3851766   -.3810148
        273  |  -.0989064   .0154318    -6.41   0.000    -.1291521   -.0686607
        275  |          0  (empty)
        276  |   -.274765   .0004817  -570.36   0.000    -.2757092   -.2738208
        284  |          0  (empty)
        285  |  -1.000736    .001024  -977.24   0.000    -1.002743   -.9987292
        286  |  -.2622163    .000791  -331.51   0.000    -.2637666    -.260666
        287  |   .4608712   .0013399   343.96   0.000      .458245    .4634973
        288  |   .9550611   .0088769   107.59   0.000     .9376627    .9724595
        290  |          0  (empty)
        291  |  -.1212288   .0032774   -36.99   0.000    -.1276524   -.1148052
        297  |   -.539753   .0016117  -334.90   0.000    -.5429118   -.5365942
        300  |  -.3985166   .0008906  -447.48   0.000    -.4002621   -.3967711
        301  |   .2708742   .0039815    68.03   0.000     .2630706    .2786777
        304  |  -.1435512   .0088769   -16.17   0.000    -.1609496   -.1261527
        305  |   -.490892   .0008773  -559.56   0.000    -.4926114   -.4891725
        309  |   -.617422   .0020606  -299.63   0.000    -.6214607   -.6133832
        312  |  -.8070086   .0072491  -111.33   0.000    -.8212166   -.7928007
        316  |          0  (empty)
        317  |  -.5312065   .0007939  -669.07   0.000    -.5327626   -.5296504
        320  |  -1.231096   .0029018  -424.26   0.000    -1.236783   -1.225409
        322  |  -.2462986   .0005873  -419.34   0.000    -.2474498   -.2451475
        398  |  -.5996359   .0024382  -245.93   0.000    -.6044147   -.5948571
        399  |   .5645511   .0006942   813.20   0.000     .5631904    .5659117
        400  |  -2.067326   .0030739  -672.53   0.000    -2.073351   -2.061301
        402  |  -.1670199   .0003031  -551.12   0.000    -.1676138   -.1664259
        403  |    .399695   .0005813   687.59   0.000     .3985557    .4008344
        405  |  -.4558819   .0042456  -107.38   0.000    -.4642032   -.4475607
        407  |  -.0989064   .0154318    -6.41   0.000    -.1291521   -.0686607
        408  |  -1.494225   .0104658  -142.77   0.000    -1.514738   -1.473713
        410  |  -.1648315   .0008885  -185.51   0.000     -.166573     -.16309
        418  |  -1.225539   .0001115 -1.1e+04   0.000    -1.225757    -1.22532
        422  |   -.318433   .0016045  -198.46   0.000    -.3215779   -.3152882
        423  |  -.1137874   .0073298   -15.52   0.000    -.1281535   -.0994214
        425  |   .4102311   .0011017   372.38   0.000     .4080719    .4123903
        426  |   .1498683   .0010229   146.52   0.000     .1478635    .1518731
        430  |  -.8366984   .0088769   -94.26   0.000    -.8540968   -.8192999
        433  |          0  (empty)
        434  |   .3896591   .0033453   116.48   0.000     .3831025    .3962157
        436  |   .9262853   .0013162   703.77   0.000     .9237056    .9288649
        440  |   1.465887   .0088769   165.13   0.000     1.448488    1.483285
        441  |   .7294413   .0006088  1198.11   0.000     .7282481    .7306346
        444  |  -.4053065   .0013068  -310.15   0.000    -.4078679   -.4027452
        445  |   .2007723   .0002014   996.78   0.000     .2003775    .2011671
        475  |   .6331094   .0007285   869.12   0.000     .6316817    .6345372
        478  |          0  (empty)
        481  |   .3091686   .0001246  2480.76   0.000     .3089244    .3094129
        484  |          0  (empty)
        491  |  -1.187954   .0000287 -4.1e+04   0.000     -1.18801   -1.187898
        494  |  -.1240586   .0017364   -71.45   0.000    -.1274618   -.1206554
        498  |  -.7920536   .0154318   -51.33   0.000    -.8222993   -.7618079
        499  |   .1866811   .0024477    76.27   0.000     .1818837    .1914785
        500  |   .3113622   .0013204   235.81   0.000     .3087743    .3139501
        503  |  -.4193198   .0001065 -3937.75   0.000    -.4195285    -.419111
        505  |  -.5312065   .0007939  -669.07   0.000    -.5327626   -.5296504
        507  |   .0312237    .002363    13.21   0.000     .0265923    .0358551
        508  |  -.4121349   .0015027  -274.26   0.000    -.4150802   -.4091896
        529  |  -.5930068     .00228  -260.09   0.000    -.5974756    -.588538
        531  |  -.0718662   .0012403   -57.94   0.000    -.0742971   -.0694353
        535  |   .4486838   .0011969   374.86   0.000     .4463378    .4510297
        536  |  -.5289756    .002008  -263.43   0.000    -.5329112     -.52504
        538  |          0  (empty)
        541  |  -.7058961   .0049381  -142.95   0.000    -.7155746   -.6962175
        543  |   .1220667   .0003137   389.07   0.000     .1214518    .1226816
        545  |  -.3993726   .0084558   -47.23   0.000    -.4159456   -.3827996
        560  |  -.1212288   .0032774   -36.99   0.000    -.1276524   -.1148052
        562  |  -.7970465   .0126957   -62.78   0.000    -.8219296   -.7721634
        563  |   .2887489   .0057609    50.12   0.000     .2774578    .3000401
        564  |  -.0997989   .0009769  -102.16   0.000    -.1017136   -.0978841
        576  |          0  (empty)
        577  |   .1526701   .0003463   440.88   0.000     .1519914    .1533488
        578  |   .1929547   .0009726   198.40   0.000     .1910485    .1948609
        581  |   .2220449   .0001863  1191.77   0.000     .2216797    .2224101
        583  |  -.1212288   .0032774   -36.99   0.000    -.1276524   -.1148052
        584  |   .0738842   .0002636   280.30   0.000     .0733676    .0744009
        588  |   .1590436   .0007404   214.80   0.000     .1575924    .1604948
        592  |   .1245716   .0016594    75.07   0.000     .1213193    .1278239
        593  |  -.5347496   .0005906  -905.51   0.000    -.5359071   -.5335921
        595  |          0  (empty)
        598  |  -.6685065    .001263  -529.32   0.000    -.6709818   -.6660311
        599  |          0  (empty)
        601  |  -.5998674   .0014515  -413.27   0.000    -.6027123   -.5970226
        604  |   .3504964   .0042078    83.30   0.000     .3422492    .3587436
        607  |  -.0583863   .0000676  -863.40   0.000    -.0585189   -.0582538
        608  |    -.12317   .0022203   -55.48   0.000    -.1275217   -.1188184
        609  |  -.2312236   .0009365  -246.90   0.000    -.2330591   -.2293881
        611  |          0  (empty)
        614  |  -.1871843   .0036736   -50.95   0.000    -.1943845   -.1799841
        615  |  -1.205029   .0113052  -106.59   0.000    -1.227186   -1.182871
        620  |   .7138609   .0033324   214.22   0.000     .7073296    .7203922
        623  |  -.3066168   .0015954  -192.19   0.000    -.3097437   -.3034898
        624  |    .342466   .0038915    88.00   0.000     .3348387    .3500933
        625  |  -.1268098   .0002381  -532.53   0.000    -.1272765   -.1263431
        626  |  -.1272623   8.29e-06 -1.5e+04   0.000    -.1272786   -.1272461
        630  |          0  (empty)
        631  |          0  (empty)
        635  |   .1912351   .0010076   189.78   0.000     .1892602    .1932101
        636  |          0  (empty)
        638  |  -.3261327   .0000934 -3491.98   0.000    -.3263157   -.3259496
        678  |  -.1852432   .0014274  -129.78   0.000    -.1880408   -.1824456
        680  |   .0522645   .0001042   501.37   0.000     .0520602    .0524688
        683  |   1.023799   .0013884   737.41   0.000     1.021078     1.02652
        684  |  -.5844172   .0028459  -205.35   0.000    -.5899951   -.5788393
        687  |   .2708742   .0039815    68.03   0.000     .2630706    .2786777
        689  |          0  (empty)
        691  |  -.1033147   .0000884 -1168.66   0.000    -.1034879   -.1031414
        694  |  -.9153546    .000143 -6399.20   0.000    -.9156349   -.9150742
        697  |  -.6099838   .0014775  -412.86   0.000    -.6128796   -.6070881
        698  |  -.2454275   .0009115  -269.27   0.000     -.247214   -.2436411
        704  |          0  (empty)
        707  |          0  (empty)
        710  |  -2.611472   .0002563 -1.0e+04   0.000    -2.611974   -2.610969
        729  |  -.1586285    .001797   -88.27   0.000    -.1621506   -.1551063
        732  |  -.8070086   .0072491  -111.33   0.000    -.8212166   -.7928007
        734  |  -.0849847   .0007871  -107.98   0.000    -.0865273   -.0834421
        738  |          0  (empty)
        740  |   -.116764   .0057089   -20.45   0.000    -.1279531   -.1055748
        743  |  -.1755413   .0009689  -181.17   0.000    -.1774404   -.1736423
        746  |   .3553337   .0002605  1364.17   0.000     .3548231    .3558442
        747  |    .471069   .0020041   235.05   0.000      .467141     .474997
        748  |  -.1212288   .0032774   -36.99   0.000    -.1276524   -.1148052
        749  |  -.5222769   .0056567   -92.33   0.000    -.5333639     -.51119
        751  |          0  (empty)
        753  |   .2193328   .0004069   539.07   0.000     .2185353    .2201303
        755  |   .2798194   .0008981   311.56   0.000      .278059    .2815797
        758  |  -.5905981   .0062006   -95.25   0.000     -.602751   -.5784453
        759  |   .3739652   .0004488   833.24   0.000     .3730855    .3748448
        761  |   .1614756   .0005851   275.98   0.000     .1603288    .1626223
        762  |  -.2383621   .0011981  -198.96   0.000    -.2407102   -.2360139
        765  |  -.2156274    .000665  -324.24   0.000    -.2169308    -.214324
        768  |   .1280414   .0013686    93.55   0.000      .125359    .1307239
        777  |  -.1212288   .0032774   -36.99   0.000    -.1276524   -.1148052
        778  |  -.8070086   .0072491  -111.33   0.000    -.8212166   -.7928007
        781  |    .086731   .0006041   143.57   0.000     .0855469     .087915
        783  |          0  (empty)
        785  |  -.6135007   .0055137  -111.27   0.000    -.6243073    -.602694
        790  |  -.5964385   .0004127 -1445.07   0.000    -.5972475   -.5956296
        791  |  -.1212288   .0032774   -36.99   0.000    -.1276524   -.1148052
        831  |          0  (empty)
        832  |  -.8070086   .0072491  -111.33   0.000    -.8212166   -.7928007
        833  |          0  (empty)
        834  |   1.488375   .0034583   430.38   0.000     1.481597    1.495153
        837  |   .0478156   .0008134    58.79   0.000     .0462214    .0494098
        845  |  -.3488637   .0008171  -426.98   0.000    -.3504651   -.3472623
        846  |          0  (empty)
        848  |          0  (empty)
        849  |   .6337576    .001082   585.75   0.000      .631637    .6358781
        850  |  -.2855779   .0022698  -125.81   0.000    -.2900267   -.2811291
        851  |  -.2403396   .0025464   -94.38   0.000    -.2453304   -.2353488
        853  |  -.3300111   .0015304  -215.64   0.000    -.3330106   -.3270116
        854  |          0  (empty)
        858  |  -.0749992   .0009875   -75.95   0.000    -.0769347   -.0730637
        859  |   -.821891    .000855  -961.26   0.000    -.8235667   -.8202152
        886  |  -2.604614   .0039841  -653.76   0.000    -2.612423   -2.596805
        889  |  -1.360601    .003376  -403.02   0.000    -1.367218   -1.353984
        892  |  -.1323907   .0028009   -47.27   0.000    -.1378803    -.126901
        893  |  -.0989064   .0154318    -6.41   0.000    -.1291521   -.0686607
        895  |   -.140369   .0012963  -108.29   0.000    -.1429097   -.1378284
        905  |  -.0254411   .0002775   -91.67   0.000    -.0259851   -.0248971
        908  |   -.294253   .0008695  -338.42   0.000    -.2959572   -.2925489
        915  |   .5794334   .0074099    78.20   0.000     .5649103    .5939564
        918  |          0  (empty)
        921  |          0  (empty)
        924  |  -.1212288   .0032774   -36.99   0.000    -.1276524   -.1148052
        925  |  -.4307566   .0000752 -5730.83   0.000    -.4309039   -.4306092
        927  |  -.2133742   .0020248  -105.38   0.000    -.2173427   -.2094056
        931  |  -.5738628    .001555  -369.04   0.000    -.5769105   -.5708151
        935  |   .7607192   .0030934   245.92   0.000     .7546563    .7667821
        936  |          0  (empty)
        946  |   .5527744   .0016089   343.58   0.000     .5496211    .5559278
        977  |  -.1669065   6.79e-07 -2.5e+05   0.000    -.1669079   -.1669052
        980  |  -1.451333   .0013362 -1086.14   0.000    -1.453952   -1.448714
        981  |  -.0576164   .0003517  -163.80   0.000    -.0583058    -.056927
        989  |  -.5303143   .0012794  -414.49   0.000    -.5328219   -.5278066
        992  |  -.5356653   .0016308  -328.47   0.000    -.5388615    -.532469
             |
       _cons |   .0989064   .0154318     6.41   0.000     .0686607    .1291521
------------------------------------------------------------------------------

. estadd local Controls "No"

added macro:
           e(Controls) : "No"

. estadd local Controls1 " "

added macro:
          e(Controls1) : " "

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "Yes"

added macro:
          e(Controls5) : "Yes"

. mean voted22 if e(sample)==1 & treatedf==0

Mean estimation                         Number of obs = 14,785

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .4949611    .004112      .4869011    .5030211
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M4

. eststo M5:  logit voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool i.kunta19, cl
> uster(kunta19)

note: 16.kunta19 != 0 predicts success perfectly;
      16.kunta19 omitted and 1 obs not used.

note: 18.kunta19 != 0 predicts success perfectly;
      18.kunta19 omitted and 1 obs not used.

note: 47.kunta19 != 0 predicts success perfectly;
      47.kunta19 omitted and 1 obs not used.

note: 78.kunta19 != 0 predicts failure perfectly;
      78.kunta19 omitted and 1 obs not used.

note: 81.kunta19 != 0 predicts failure perfectly;
      81.kunta19 omitted and 2 obs not used.

note: 82.kunta19 != 0 predicts success perfectly;
      82.kunta19 omitted and 1 obs not used.

note: 99.kunta19 != 0 predicts failure perfectly;
      99.kunta19 omitted and 2 obs not used.

note: 105.kunta19 != 0 predicts success perfectly;
      105.kunta19 omitted and 1 obs not used.

note: 143.kunta19 != 0 predicts failure perfectly;
      143.kunta19 omitted and 2 obs not used.

note: 152.kunta19 != 0 predicts success perfectly;
      152.kunta19 omitted and 1 obs not used.

note: 171.kunta19 != 0 predicts failure perfectly;
      171.kunta19 omitted and 2 obs not used.

note: 176.kunta19 != 0 predicts failure perfectly;
      176.kunta19 omitted and 2 obs not used.

note: 178.kunta19 != 0 predicts failure perfectly;
      178.kunta19 omitted and 1 obs not used.

note: 181.kunta19 != 0 predicts failure perfectly;
      181.kunta19 omitted and 3 obs not used.

note: 204.kunta19 != 0 predicts success perfectly;
      204.kunta19 omitted and 2 obs not used.

note: 214.kunta19 != 0 predicts failure perfectly;
      214.kunta19 omitted and 4 obs not used.

note: 218.kunta19 != 0 predicts failure perfectly;
      218.kunta19 omitted and 1 obs not used.

note: 235.kunta19 != 0 predicts failure perfectly;
      235.kunta19 omitted and 1 obs not used.

note: 250.kunta19 != 0 predicts failure perfectly;
      250.kunta19 omitted and 1 obs not used.

note: 256.kunta19 != 0 predicts failure perfectly;
      256.kunta19 omitted and 1 obs not used.

note: 263.kunta19 != 0 predicts failure perfectly;
      263.kunta19 omitted and 2 obs not used.

note: 275.kunta19 != 0 predicts failure perfectly;
      275.kunta19 omitted and 1 obs not used.

note: 284.kunta19 != 0 predicts failure perfectly;
      284.kunta19 omitted and 1 obs not used.

note: 290.kunta19 != 0 predicts success perfectly;
      290.kunta19 omitted and 1 obs not used.

note: 316.kunta19 != 0 predicts failure perfectly;
      316.kunta19 omitted and 1 obs not used.

note: 433.kunta19 != 0 predicts success perfectly;
      433.kunta19 omitted and 2 obs not used.

note: 478.kunta19 != 0 predicts failure perfectly;
      478.kunta19 omitted and 1 obs not used.

note: 484.kunta19 != 0 predicts failure perfectly;
      484.kunta19 omitted and 1 obs not used.

note: 538.kunta19 != 0 predicts failure perfectly;
      538.kunta19 omitted and 1 obs not used.

note: 576.kunta19 != 0 predicts failure perfectly;
      576.kunta19 omitted and 1 obs not used.

note: 595.kunta19 != 0 predicts success perfectly;
      595.kunta19 omitted and 2 obs not used.

note: 599.kunta19 != 0 predicts failure perfectly;
      599.kunta19 omitted and 3 obs not used.

note: 611.kunta19 != 0 predicts failure perfectly;
      611.kunta19 omitted and 3 obs not used.

note: 630.kunta19 != 0 predicts success perfectly;
      630.kunta19 omitted and 1 obs not used.

note: 631.kunta19 != 0 predicts failure perfectly;
      631.kunta19 omitted and 3 obs not used.

note: 636.kunta19 != 0 predicts failure perfectly;
      636.kunta19 omitted and 2 obs not used.

note: 689.kunta19 != 0 predicts failure perfectly;
      689.kunta19 omitted and 2 obs not used.

note: 704.kunta19 != 0 predicts failure perfectly;
      704.kunta19 omitted and 3 obs not used.

note: 707.kunta19 != 0 predicts failure perfectly;
      707.kunta19 omitted and 4 obs not used.

note: 738.kunta19 != 0 predicts success perfectly;
      738.kunta19 omitted and 1 obs not used.

note: 751.kunta19 != 0 predicts failure perfectly;
      751.kunta19 omitted and 1 obs not used.

note: 783.kunta19 != 0 predicts failure perfectly;
      783.kunta19 omitted and 1 obs not used.

note: 831.kunta19 != 0 predicts failure perfectly;
      831.kunta19 omitted and 1 obs not used.

note: 833.kunta19 != 0 predicts failure perfectly;
      833.kunta19 omitted and 1 obs not used.

note: 846.kunta19 != 0 predicts failure perfectly;
      846.kunta19 omitted and 2 obs not used.

note: 848.kunta19 != 0 predicts failure perfectly;
      848.kunta19 omitted and 2 obs not used.

note: 854.kunta19 != 0 predicts success perfectly;
      854.kunta19 omitted and 2 obs not used.

note: 918.kunta19 != 0 predicts failure perfectly;
      918.kunta19 omitted and 1 obs not used.

note: 921.kunta19 != 0 predicts failure perfectly;
      921.kunta19 omitted and 1 obs not used.

note: 936.kunta19 != 0 predicts success perfectly;
      936.kunta19 omitted and 1 obs not used.

note: firstvote omitted because of collinearity.
Iteration 0:   log pseudolikelihood = -25503.398  
Iteration 1:   log pseudolikelihood = -22444.652  
Iteration 2:   log pseudolikelihood =  -22435.03  
Iteration 3:   log pseudolikelihood = -22434.989  
Iteration 4:   log pseudolikelihood = -22434.989  

Logistic regression                                     Number of obs = 36,796
                                                        Wald chi2(15) =      .
                                                        Prob > chi2   =      .
Log pseudolikelihood = -22434.989                       Pseudo R2     = 0.1203

                                (Std. err. adjusted for 210 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0523277    .025644     2.04   0.041     .0020664    .1025891
     molincome |   .1321648   .0213751     6.18   0.000     .0902704    .1740592
        female |    .044481   .0215201     2.07   0.039     .0023023    .0866596
           age |   .0402518   .0013148    30.61   0.000     .0376748    .0428288
       foreign |  -1.313813   .0650336   -20.20   0.000    -1.441277    -1.18635
               |
       moses1d |
            1  |    .963198   .0964226     9.99   0.000     .7742131    1.152183
            2  |   .4472431   .0763455     5.86   0.000     .2976087    .5968775
            3  |   .8191546   .0542729    15.09   0.000     .7127817    .9255275
            4  |   .4089383   .0540949     7.56   0.000     .3029142    .5149623
            5  |   .0402237   .0501667     0.80   0.423    -.0581011    .1385485
            6  |   .6261312   .0729277     8.59   0.000     .4831955     .769067
            7  |   .0610506   .0830929     0.73   0.463    -.1018085    .2239097
            9  |   .3005301    .116139     2.59   0.010     .0729018    .5281584
               |
     firstvote |          0  (omitted)
1.mohighschool |   .6638048   .0307219    21.61   0.000      .603591    .7240187
               |
       kunta19 |
            9  |   .2920758   .0053003    55.11   0.000     .2816874    .3024642
           10  |   -.332352   .0039444   -84.26   0.000    -.3400829   -.3246211
           16  |          0  (empty)
           18  |          0  (empty)
           19  |  -.6443053   .0090555   -71.15   0.000    -.6620538   -.6265567
           20  |  -1.219062   .0285427   -42.71   0.000    -1.275005    -1.16312
           47  |          0  (empty)
           49  |   .1928929   .0236554     8.15   0.000     .1465292    .2392565
           50  |   1.451561   .0263952    54.99   0.000     1.399827    1.503294
           51  |   .1303423   .0075083    17.36   0.000     .1156263    .1450583
           61  |   -.440556   .0388769   -11.33   0.000    -.5167534   -.3643586
           69  |   .2273352   .0055119    41.24   0.000     .2165321    .2381382
           71  |   .4811487   .0080973    59.42   0.000     .4652783    .4970191
           72  |     .74138   .0084605    87.63   0.000     .7247976    .7579624
           74  |   .2029507   .0085224    23.81   0.000     .1862471    .2196543
           75  |   .9131747   .0565912    16.14   0.000      .802258    1.024091
           77  |   .0790846   .0043299    18.26   0.000     .0705982    .0875711
           78  |          0  (empty)
           79  |  -.2332818   .0101454   -22.99   0.000    -.2531664   -.2133973
           81  |          0  (empty)
           82  |          0  (empty)
           86  |  -.4474069   .0067091   -66.69   0.000    -.4605565   -.4342574
           90  |   .6260199   .0291674    21.46   0.000     .5688528     .683187
           91  |  -.1273568   .0247746    -5.14   0.000    -.1759141   -.0787995
           92  |  -.2085349   .0116922   -17.84   0.000    -.2314511   -.1856186
           98  |   .5676053   .0197747    28.70   0.000     .5288476     .606363
           99  |          0  (empty)
          102  |   .6748011   .0329279    20.49   0.000     .6102636    .7393387
          105  |          0  (empty)
          106  |  -.6080837   .0256275   -23.73   0.000    -.6583126   -.5578548
          108  |  -.0285208   .0078298    -3.64   0.000     -.043867   -.0131746
          109  |  -.4360537   .0270756   -16.11   0.000    -.4891208   -.3829865
          111  |   1.515298   .0375221    40.38   0.000     1.441756     1.58884
          139  |   .7134921   .0354811    20.11   0.000     .6439503    .7830338
          140  |  -1.009929   .0263795   -38.28   0.000    -1.061632    -.958226
          143  |          0  (empty)
          146  |  -.7450969   .0106342   -70.07   0.000    -.7659395   -.7242543
          148  |  -.4115565   .0135201   -30.44   0.000    -.4380554   -.3850577
          151  |   .4147411   .0083084    49.92   0.000      .398457    .4310252
          152  |          0  (empty)
          153  |   .2723685   .0481406     5.66   0.000     .1780145    .3667224
          165  |  -.1099125   .0079451   -13.83   0.000    -.1254847   -.0943403
          167  |   -.082422   .0121242    -6.80   0.000    -.1061851   -.0586589
          169  |   .2372468   .0085016    27.91   0.000      .220584    .2539096
          171  |          0  (empty)
          176  |          0  (empty)
          177  |   .3309593    .007158    46.24   0.000     .3169298    .3449888
          178  |          0  (empty)
          179  |   .0665542    .013574     4.90   0.000     .0399497    .0931588
          181  |          0  (empty)
          182  |  -.2509668   .0089069   -28.18   0.000     -.268424   -.2335096
          186  |  -.1407583   .0200451    -7.02   0.000     -.180046   -.1014705
          202  |  -.1485603   .0108657   -13.67   0.000    -.1698567   -.1272638
          204  |          0  (empty)
          205  |  -.1231481   .0233142    -5.28   0.000    -.1688431    -.077453
          208  |     .22879   .0177586    12.88   0.000     .1939838    .2635963
          211  |   -.494903   .0193513   -25.57   0.000    -.5328308   -.4569752
          213  |   1.187779   .0546933    21.72   0.000     1.080582    1.294976
          214  |          0  (empty)
          216  |  -.4936641    .014862   -33.22   0.000    -.5227931   -.4645351
          217  |    .267594   .0065577    40.81   0.000     .2547412    .2804467
          218  |          0  (empty)
          224  |  -.3775083   .0110113   -34.28   0.000      -.39909   -.3559266
          226  |  -.1749744   .0062963   -27.79   0.000    -.1873149   -.1626339
          230  |   .2292315   .0085126    26.93   0.000     .2125471    .2459158
          231  |  -.2497051   .0139021   -17.96   0.000    -.2769528   -.2224575
          232  |  -.1643807   .0293797    -5.60   0.000     -.221964   -.1067975
          233  |  -.1630197   .0252055    -6.47   0.000    -.2124215   -.1136178
          235  |          0  (empty)
          236  |   .4074556   .0080011    50.93   0.000     .3917738    .4231375
          239  |   .9364575   .0118495    79.03   0.000     .9132329     .959682
          240  |    -.04329   .0100318    -4.32   0.000    -.0629519   -.0236281
          241  |  -.6305209   .0214238   -29.43   0.000    -.6725108    -.588531
          244  |   .0896592   .0105377     8.51   0.000     .0690056    .1103128
          245  |  -.1275037   .0102085   -12.49   0.000    -.1475119   -.1074954
          249  |   .7165659    .042483    16.87   0.000     .6333008    .7998309
          250  |          0  (empty)
          256  |          0  (empty)
          257  |  -.0296527   .0124972    -2.37   0.018    -.0541467   -.0051587
          260  |  -1.338703   .0258077   -51.87   0.000    -1.389285   -1.288121
          261  |  -.1358922   .0312204    -4.35   0.000     -.197083   -.0747013
          263  |          0  (empty)
          265  |  -.2445102   .0104805   -23.33   0.000    -.2650516   -.2239687
          271  |   .5964333    .027845    21.42   0.000      .541858    .6510085
          272  |    .459634   .0337092    13.64   0.000     .3935651    .5257029
          273  |   .7852783   .0633813    12.39   0.000     .6610533    .9095033
          275  |          0  (empty)
          276  |  -.3185381   .0085061   -37.45   0.000    -.3352097   -.3018665
          284  |          0  (empty)
          285  |  -.3262503   .0298921   -10.91   0.000    -.3848379   -.2676628
          286  |   .4301555   .0352657    12.20   0.000     .3610361     .499275
          287  |   .3529282   .0084613    41.71   0.000     .3363444     .369512
          288  |   1.429112   .0278503    51.31   0.000     1.374526    1.483697
          290  |          0  (empty)
          291  |   .5775821   .0544929    10.60   0.000     .4707779    .6843863
          297  |   .0575452   .0254398     2.26   0.024     .0076841    .1074063
          300  |  -.4309508   .0060315   -71.45   0.000    -.4427722   -.4191293
          301  |   .9015932   .0260884    34.56   0.000     .8504608    .9527255
          304  |   .1828442   .0304048     6.01   0.000     .1232519    .2424365
          305  |  -.4004353    .005022   -79.74   0.000    -.4102782   -.3905924
          309  |  -.4016206   .0076319   -52.62   0.000    -.4165789   -.3866623
          312  |   .8473967   .0317791    26.67   0.000     .7851109    .9096825
          316  |          0  (empty)
          317  |   .8907837   .0476603    18.69   0.000     .7973712    .9841962
          320  |  -.6087495   .0381279   -15.97   0.000    -.6834789   -.5340201
          322  |  -.0966202    .007557   -12.79   0.000    -.1114316   -.0818089
          398  |  -.0853874   .0221747    -3.85   0.000     -.128849   -.0419258
          399  |   1.639215   .0459264    35.69   0.000     1.549201    1.729229
          400  |  -1.078609   .0285228   -37.82   0.000    -1.134513   -1.022706
          402  |   .0093264   .0051039     1.83   0.068     -.000677    .0193298
          403  |   .3594193   .0058488    61.45   0.000     .3479557    .3708828
          405  |   .1218928   .0264701     4.60   0.000     .0700123    .1737733
          407  |   1.127848   .0538021    20.96   0.000     1.022397    1.233298
          408  |  -.6541658   .0373618   -17.51   0.000    -.7273937    -.580938
          410  |  -.0192175   .0084828    -2.27   0.023    -.0358435   -.0025916
          418  |  -.4440193   .0277079   -16.02   0.000    -.4983259   -.3897128
          422  |  -.0912102   .0075176   -12.13   0.000    -.1059444    -.076476
          423  |   .9069747   .0323366    28.05   0.000     .8435962    .9703533
          425  |   .4875495   .0083041    58.71   0.000     .4712738    .5038252
          426  |   .2750038   .0052776    52.11   0.000       .26466    .2853477
          430  |   .0171444   .0513106     0.33   0.738    -.0834225    .1177113
          433  |          0  (empty)
          434  |   .8225842   .0197868    41.57   0.000     .7838028    .8613657
          436  |   1.250423   .0118952   105.12   0.000     1.227109    1.273737
          440  |   1.954244   .0230528    84.77   0.000     1.909062    1.999427
          441  |    .779205   .0073782   105.61   0.000      .764744    .7936661
          444  |  -.4543729   .0085736   -53.00   0.000     -.471177   -.4375689
          445  |   .1713556   .0089559    19.13   0.000     .1538025    .1889088
          475  |   .8728761     .00724   120.56   0.000     .8586861    .8870661
          478  |          0  (empty)
          481  |   .1766718   .0085032    20.78   0.000     .1600059    .1933378
          484  |          0  (empty)
          491  |  -.8045145   .0229372   -35.07   0.000    -.8494706   -.7595583
          494  |   .0982651   .0085765    11.46   0.000     .0814554    .1150748
          498  |   .4147059   .0690574     6.01   0.000     .2793559     .550056
          499  |   .4970909   .0131544    37.79   0.000     .4713088    .5228729
          500  |   .1739254   .0103526    16.80   0.000     .1536347    .1942161
          503  |  -.4724118   .0069772   -67.71   0.000     -.486087   -.4587367
          505  |  -.1604786   .0145943   -11.00   0.000    -.1890829   -.1318743
          507  |  -.0931192   .0053284   -17.48   0.000    -.1035627   -.0826757
          508  |   .2590395    .017216    15.05   0.000     .2252966    .2927823
          529  |  -.0704778   .0170877    -4.12   0.000    -.1039691   -.0369864
          531  |  -.0923492   .0081981   -11.26   0.000    -.1084171   -.0762813
          535  |   .6616954    .009434    70.14   0.000     .6432052    .6801856
          536  |   .0001755   .0242658     0.01   0.994    -.0473847    .0477357
          538  |          0  (empty)
          541  |  -.4651355    .021444   -21.69   0.000    -.5071649    -.423106
          543  |   .0767627   .0092534     8.30   0.000     .0586264     .094899
          545  |  -.0169782   .0220923    -0.77   0.442    -.0602782    .0263219
          560  |   .6499451    .030816    21.09   0.000     .5895469    .7103434
          562  |  -.5707466   .0302885   -18.84   0.000    -.6301109   -.5113822
          563  |     .51032   .0173755    29.37   0.000     .4762647    .5443753
          564  |   .0806468    .012862     6.27   0.000     .0554376    .1058559
          576  |          0  (empty)
          577  |   .0518924   .0093009     5.58   0.000     .0336629    .0701219
          578  |   .4566175   .0071294    64.05   0.000     .4426441     .470591
          581  |   .3185935   .0072756    43.79   0.000     .3043336    .3328534
          583  |   .0578642   .0127986     4.52   0.000     .0327793     .082949
          584  |   .3158987   .0079098    39.94   0.000     .3003958    .3314017
          588  |   .2844754   .0107915    26.36   0.000     .2633244    .3056265
          592  |   .2295178   .0084591    27.13   0.000     .2129382    .2460974
          593  |  -.5603237   .0067861   -82.57   0.000    -.5736243   -.5470231
          595  |          0  (empty)
          598  |  -.1775667   .0188841    -9.40   0.000    -.2145788   -.1405546
          599  |          0  (empty)
          601  |  -.8230507   .0235993   -34.88   0.000    -.8693045   -.7767969
          604  |   .7573424   .0242325    31.25   0.000     .7098476    .8048373
          607  |  -.1026248   .0114604    -8.95   0.000    -.1250868   -.0801627
          608  |  -.1206382      .0127    -9.50   0.000    -.1455297   -.0957466
          609  |  -.1416545   .0096495   -14.68   0.000    -.1605672   -.1227418
          611  |          0  (empty)
          614  |  -.0662128   .0107996    -6.13   0.000    -.0873796   -.0450459
          615  |  -.9485266   .0219398   -43.23   0.000    -.9915277   -.9055254
          620  |   .8649992   .0099232    87.17   0.000     .8455501    .8844484
          623  |  -.5468221   .0116661   -46.87   0.000    -.5696872    -.523957
          624  |   .2635996   .0112682    23.39   0.000     .2415144    .2856849
          625  |   .0898059   .0156698     5.73   0.000     .0590937    .1205181
          626  |   .0711458   .0069322    10.26   0.000     .0575589    .0847326
          630  |          0  (empty)
          631  |          0  (empty)
          635  |   .2414852   .0081913    29.48   0.000     .2254305      .25754
          636  |          0  (empty)
          638  |  -.3639795   .0100387   -36.26   0.000     -.383655   -.3443041
          678  |   .4739588   .0284527    16.66   0.000     .4181925    .5297252
          680  |   .0833168   .0100522     8.29   0.000     .0636149    .1030187
          683  |   1.187061   .0131558    90.23   0.000     1.161276    1.212846
          684  |  -.0704954   .0228216    -3.09   0.002    -.1152249   -.0257659
          687  |   .3451098   .0180085    19.16   0.000     .3098138    .3804059
          689  |          0  (empty)
          691  |  -.0504443    .008602    -5.86   0.000     -.067304   -.0335846
          694  |   .0667715   .0311532     2.14   0.032     .0057123    .1278306
          697  |  -.7349541    .010237   -71.79   0.000    -.7550183   -.7148898
          698  |  -.0545602   .0109947    -4.96   0.000    -.0761094    -.033011
          704  |          0  (empty)
          707  |          0  (empty)
          710  |  -2.164042   .0127611  -169.58   0.000    -2.189053   -2.139031
          729  |  -.0107188    .006143    -1.74   0.081    -.0227589    .0013213
          732  |  -.7194885    .016345   -44.02   0.000     -.751524    -.687453
          734  |  -.1031251   .0046456   -22.20   0.000    -.1122303   -.0940198
          738  |          0  (empty)
          740  |   .4996751   .0306358    16.31   0.000     .4396299    .5597203
          743  |   .1954412   .0215202     9.08   0.000     .1532625      .23762
          746  |   .6348935   .0089978    70.56   0.000     .6172581    .6525288
          747  |   .3422353   .0089942    38.05   0.000      .324607    .3598636
          748  |   1.331231   .0595201    22.37   0.000     1.214573    1.447888
          749  |   -.284505   .0334032    -8.52   0.000     -.349974    -.219036
          751  |          0  (empty)
          753  |   .1187383   .0096196    12.34   0.000     .0998843    .1375924
          755  |   .6090706   .0260476    23.38   0.000     .5580181     .660123
          758  |   -.551243   .0117173   -47.05   0.000    -.5742086   -.5282775
          759  |   .5827721   .0090397    64.47   0.000     .5650546    .6004895
          761  |   .1170082   .0045877    25.50   0.000     .1080164        .126
          762  |  -.2744606   .0056478   -48.60   0.000    -.2855301    -.263391
          765  |  -.3010614   .0052649   -57.18   0.000    -.3113804   -.2907424
          768  |   .1215875     .00798    15.24   0.000      .105947     .137228
          777  |    .297829   .0347347     8.57   0.000     .2297503    .3659077
          778  |   .1163169   .0376814     3.09   0.002     .0424627    .1901711
          781  |    .241812   .0119445    20.24   0.000     .2184012    .2652229
          783  |          0  (empty)
          785  |  -.5756435   .0126212   -45.61   0.000    -.6003806   -.5509065
          790  |   .0485888   .0224247     2.17   0.030     .0046372    .0925404
          791  |   .8157755   .0265524    30.72   0.000     .7637338    .8678173
          831  |          0  (empty)
          832  |   .1403246   .0267913     5.24   0.000     .0878147    .1928346
          833  |          0  (empty)
          834  |   1.480799   .0194259    76.23   0.000     1.442725    1.518874
          837  |   .2223661   .0144624    15.38   0.000     .1940204    .2507119
          845  |  -.2682368   .0086858   -30.88   0.000    -.2852606   -.2512129
          846  |          0  (empty)
          848  |          0  (empty)
          849  |   .9547092   .0097874    97.54   0.000     .9355263    .9738922
          850  |  -.5220616   .0168532   -30.98   0.000    -.5550933     -.48903
          851  |   .1212672   .0186106     6.52   0.000     .0847911    .1577434
          853  |   .2562785   .0254617    10.07   0.000     .2063744    .3061825
          854  |          0  (empty)
          858  |   .4052426    .020446    19.82   0.000     .3651693     .445316
          859  |   .0687356   .0246507     2.79   0.005     .0204211    .1170502
          886  |  -1.585616   .0260686   -60.82   0.000    -1.636709   -1.534522
          889  |  -1.197642   .0098801  -121.22   0.000    -1.217007   -1.178277
          892  |   .7409764   .0328657    22.55   0.000     .6765608     .805392
          893  |    .890789   .0356611    24.98   0.000     .8208946    .9606834
          895  |   .0175957    .006091     2.89   0.004     .0056575     .029534
          905  |   .0208763   .0102197     2.04   0.041      .000846    .0409065
          908  |  -.1967877   .0095769   -20.55   0.000    -.2155582   -.1780173
          915  |   1.603078   .0320637    50.00   0.000     1.540234    1.665921
          918  |          0  (empty)
          921  |          0  (empty)
          924  |   .8474183   .0329182    25.74   0.000     .7828999    .9119368
          925  |  -.5053334   .0118395   -42.68   0.000    -.5285385   -.4821283
          927  |  -.3274122   .0100395   -32.61   0.000    -.3470893   -.3077352
          931  |  -.5057035   .0068489   -73.84   0.000    -.5191271   -.4922799
          935  |   .8447395   .0146787    57.55   0.000     .8159697    .8735093
          936  |          0  (empty)
          946  |   .7251326   .0050385   143.92   0.000     .7152574    .7350078
          977  |   .0779862   .0085652     9.11   0.000     .0611988    .0947736
          980  |  -.5913821   .0341862   -17.30   0.000    -.6583859   -.5243784
          981  |  -.1741619     .01291   -13.49   0.000    -.1994651   -.1488587
          989  |  -.5089038   .0055326   -91.98   0.000    -.5197475   -.4980601
          992  |  -.0366663   .0187926    -1.95   0.051    -.0734991    .0001664
               |
         _cons |  -3.644367   .2453893   -14.85   0.000    -4.125321   -3.163412
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "Yes"

added macro:
          e(Controls5) : "Yes"

. mean voted22 if e(sample)==1 & treatedf==0

Mean estimation                         Number of obs = 14,637

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .4965498   .0041328       .488449    .5046507
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M5

. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local numbers "& (1) & (2) & (3) & (4) & (5) \\ \hline"

. local emptyrow "& & & & &  \\ "

. local line "& & & & & \hline \\ "

. 
. local dstars " "

. local sestars " "

. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("Controls Controls" "Controls1 \phantom{controls}"  "Controls2 \phantom{controls}" "Controls3 \phantom{c
> ontrols}" "Controls4 \phantom{controls}"  "Controls5 Municipality FE" "umean Untreated $\bar{Y}$" "N Observation
> s") ///
> sfmt(%9.3f %9.3f %9.3f %9.3f  %9.3f  %9.3f  %9.3f %9.0fc) mlabels(none) nonumbers posthead("`header'" "`emptyrow
> '" "`numbers'" "`emptyrow'") ///
> refcat(treatedf "", nolabel below) title("Spillovers Average Treatment Effect - Logit Model") nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

Spillovers Average Treatment Effect - Logit Model
----------------------------------------------------------------------------------------------------
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & & &  \\ 
& (1) & (2) & (3) & (4) & (5) \\ \hline
& & & & &  \\ 
voted22                                                                                             
Treated in HH               0.054**         0.060**         0.060**         0.045*          0.052** 
                          (0.025)         (0.024)         (0.026)         (0.024)         (0.026)   

                                                                                                    
----------------------------------------------------------------------------------------------------
Controls                       No    Female, Age, Immigrant,    Female, Age, Immigrant,              No    Female,
>  Age, Immigrant,   
\phantom{controls}                      Ln Income      Ln Income,                      Ln Income,   
\phantom{controls}                                   SES Background,                    SES Background,   
\phantom{controls}                                   Educational Background,                    Educational Backgr
> ound,   
\phantom{controls}                                   First Time Eligble to Vote                    First Time Elig
> ble to Vote   
Municipality FE                No              No              No             Yes             Yes   
Untreated $\bar{Y}$         0.494           0.496           0.496           0.495           0.497   
Observations               37,207          36,876          36,876          37,126          36,796   
----------------------------------------------------------------------------------------------------

. 
. 
. 
. *************************************************
. *****TABLE A10
. *************************************************
. 
. 
. 
. eststo clear

. 
. eststo pooled: reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(kun
> ta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     36,876
                                                F(14, 259)        =     612.45
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1407
                                                Root MSE          =     .46358

                                (Std. err. adjusted for 260 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0130222   .0055449     2.35   0.020     .0021033     .023941
     molincome |   .0248605   .0040409     6.15   0.000     .0169033    .0328177
        female |   .0095634   .0045088     2.12   0.035     .0006849     .018442
           age |   .0086664   .0002753    31.48   0.000     .0081243    .0092085
       foreign |  -.2464759   .0128641   -19.16   0.000    -.2718074   -.2211443
               |
       moses1d |
            1  |   .2246265   .0207849    10.81   0.000     .1836976    .2655553
            2  |   .0959936   .0167937     5.72   0.000     .0629242    .1290631
            3  |   .1767397     .01162    15.21   0.000      .153858    .1996215
            4  |   .0857361   .0117858     7.27   0.000     .0625278    .1089443
            5  |   .0027727   .0104861     0.26   0.792    -.0178761    .0234216
            6  |   .1327165   .0162459     8.17   0.000     .1007256    .1647075
            7  |   .0146583   .0174838     0.84   0.403    -.0197702    .0490868
            9  |   .0622016   .0236856     2.63   0.009     .0155608    .1088425
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1455762   .0079108    18.40   0.000     .1299985    .1611539
         _cons |  -.2481974   .0485264    -5.11   0.000    -.3437539   -.1526408
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store pooled

. 
. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: pooled

. 
. qui: reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool if treatedf1!=., clust
> er(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool if treatedf2!=., clust
> er(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo neutral: reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool if treatedf
> 1!=., cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     22,028
                                                F(14, 235)        =     427.83
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1362
                                                Root MSE          =     .46485

                                (Std. err. adjusted for 236 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0214017   .0080949     2.64   0.009     .0054537    .0373496
     molincome |   .0270655   .0055818     4.85   0.000     .0160687    .0380623
        female |    .011232   .0054758     2.05   0.041     .0004441    .0220199
           age |   .0082983   .0002985    27.80   0.000     .0077103    .0088863
       foreign |  -.2402055   .0176077   -13.64   0.000    -.2748947   -.2055164
               |
       moses1d |
            1  |   .2156183   .0239351     9.01   0.000     .1684635     .262773
            2  |   .0997757   .0214804     4.64   0.000     .0574569    .1420944
            3  |   .1880735   .0141969    13.25   0.000     .1601041    .2160429
            4  |   .0882635   .0136523     6.47   0.000     .0613669      .11516
            5  |   .0020079   .0146057     0.14   0.891     -.026767    .0307827
            6  |   .1356991   .0223021     6.08   0.000     .0917615    .1796367
            7  |   .0259093   .0204437     1.27   0.206    -.0143671    .0661856
            9  |   .0698826   .0289842     2.41   0.017     .0127806    .1269847
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1381832   .0099837    13.84   0.000     .1185143    .1578521
         _cons |  -.2579644   .0582204    -4.43   0.000     -.372665   -.1432638
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Neutral"

added macro:
             e(label1) : "Neutral"

. estadd local label2 "- Expressive"

added macro:
             e(label2) : "- Expressive"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store neutral

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: neutral

. 
. qui: reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool if treatedf2!=., clust
> er(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool if treatedf3!=., clust
> er(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo expressive: reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool if treat
> edf2!=., cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     22,113
                                                F(14, 241)        =     371.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1409
                                                Root MSE          =     .46359

                                (Std. err. adjusted for 242 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0058419   .0078409     0.75   0.457    -.0096035    .0212873
     molincome |   .0255528   .0047889     5.34   0.000     .0161194    .0349861
        female |   .0134164   .0059477     2.26   0.025     .0017004    .0251324
           age |   .0086531   .0002728    31.72   0.000     .0081157    .0091905
       foreign |  -.2343517   .0177147   -13.23   0.000    -.2692472   -.1994562
               |
       moses1d |
            1  |   .2152136   .0221203     9.73   0.000     .1716397    .2587875
            2  |    .113303   .0248513     4.56   0.000     .0643496    .1622565
            3  |    .177151   .0170037    10.42   0.000     .1436562    .2106459
            4  |    .084889   .0164968     5.15   0.000     .0523927    .1173852
            5  |     .00614   .0169619     0.36   0.718    -.0272726    .0395526
            6  |   .1395639   .0267208     5.22   0.000     .0869278    .1921999
            7  |   .0036675   .0245419     0.15   0.881    -.0446765    .0520114
            9  |   .0578508    .028782     2.01   0.046     .0011544    .1145472
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1505358   .0086507    17.40   0.000     .1334953    .1675764
         _cons |  -.2604676   .0490965    -5.31   0.000    -.3571807   -.1637545
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Expressive"

added macro:
             e(label1) : "Expressive"

. estadd local label2 "- Informative"

added macro:
             e(label2) : "- Informative"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store expressive

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: expressive

. 
. 
. qui: reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool if treatedf3!=., clust
> er(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool if treatedf1!=., clust
> er(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo informative: reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool if trea
> tedf3!=., cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     22,059
                                                F(14, 242)        =     477.94
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1400
                                                Root MSE          =     .46383

                                (Std. err. adjusted for 243 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0117743   .0076579     1.54   0.125    -.0033105     .026859
     molincome |   .0248334   .0053619     4.63   0.000     .0142715    .0353952
        female |   .0147006   .0066586     2.21   0.028     .0015844    .0278169
           age |   .0087272   .0002985    29.24   0.000     .0081393    .0093152
       foreign |  -.2431511   .0164002   -14.83   0.000    -.2754565   -.2108457
               |
       moses1d |
            1  |   .2450281   .0272705     8.99   0.000     .1913103     .298746
            2  |   .1102726   .0225426     4.89   0.000     .0658679    .1546774
            3  |   .1826259   .0135448    13.48   0.000     .1559451    .2093067
            4  |   .0902436   .0150419     6.00   0.000     .0606137    .1198734
            5  |   .0105545   .0134123     0.79   0.432    -.0158653    .0369743
            6  |    .145209   .0214871     6.76   0.000     .1028834    .1875346
            7  |   .0110659   .0222321     0.50   0.619    -.0327273    .0548591
            9  |   .0750576   .0271558     2.76   0.006     .0215657    .1285494
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1409878   .0105603    13.35   0.000      .120186    .1617896
         _cons |  -.2575256   .0538258    -4.78   0.000    -.3635525   -.1514987
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Informative"

added macro:
             e(label1) : "Informative"

. estadd local label2 "- Neutral"

added macro:
             e(label2) : "- Neutral"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store informative

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: informative

. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 `"Treatment: & Pooled & "Neutral" & "Expressive" & "Informative"\\ "'

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. esttab, keep (treatedf*) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers posthead("`header'" "`emptyrow'" `"`titles1'"' "`numbers'
> " "`emptyrow'") ///
> refcat(treatedf "", nolabel below) title("Spillovers - Different Treatments") nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

Spillovers - Different Treatments
------------------------------------------------------------------------------------
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & &  \\ 
Treatment: & Pooled & "Neutral" & "Expressive" & "Informative"\\ 
& (1) & (2) & (3) & (4) \\ \hline
& & & &  \\ 
Treated in HH               0.013**         0.021***        0.006           0.012   
                          (0.006)         (0.008)         (0.008)         (0.008)   

                                                                                    
------------------------------------------------------------------------------------
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.496           0.496           0.496           0.496   
Observations               36,876          22,028          22,113          22,059   
\hline                                    Neutral      Expressive     Informative   
\phantom{label2}                     - Expressive    - Informative       - Neutral   
Differences                                 0.016          -0.006          -0.010   
\phantom{se}                              (0.011)         (0.011)         (0.011)   
------------------------------------------------------------------------------------

. 
. 
. *************************************************
. *****TABLE 7
. *************************************************
. *Panel A
. *Create voting propensity groups for the direct effect sample
. sum pvote_mother if voted22!=.  & treated!=. & female!=., detail

                         Pr(voted22)
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0714104        .023837
 5%      .118203       .0244854
10%     .1504558       .0245744       Obs              49,458
25%     .2063651       .0256294       Sum of wgt.      49,458

50%     .2950955                      Mean           .3090878
                        Largest       Std. dev.      .1320126
75%     .4018736       .8301099
90%     .4902918       .8364667       Variance       .0174273
95%     .5421607       .8550987       Skewness       .4419521
99%     .6177514       .8821674       Kurtosis       2.669571

. gen marginal=.
(3,574,284 missing values generated)

. replace marginal=1 if pvote_mother>=r(p25) & pvote_mother<r(p75)
(1,616,830 real changes made)

. replace marginal=0 if pvote_mother<r(p25) | (pvote_mother>=r(p75) & pvote_mother!=.)
(1,665,387 real changes made)

. 
. gen never=.
(3,574,284 missing values generated)

. replace never=1 if pvote_mother<r(p25)
(857,917 real changes made)

. replace never=0 if pvote_mother>=r(p25) & pvote_mother!=.
(2,424,300 real changes made)

. 
. gen always=.
(3,574,284 missing values generated)

. replace always=1 if pvote_mother>=r(p75) & pvote_mother!=.
(807,470 real changes made)

. replace always=0 if pvote_mother<r(p75)
(2,474,747 real changes made)

. 
. 
. 
. eststo clear

. 
. eststo all: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal!=., 
> cluster(kunta19)

Linear regression                               Number of obs     =     49,458
                                                F(15, 252)        =     304.85
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0622
                                                Root MSE          =     .44966

                                (Std. err. adjusted for 253 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0087153    .003324     2.62   0.009     .0021689    .0152617
     molincome |   .0026628   .0038812     0.69   0.493    -.0049809    .0103066
        female |   .1048359   .0064817    16.17   0.000     .0920706    .1176012
           age |   .0075299   .0010074     7.47   0.000     .0055459     .009514
               |
       moses1d |
            1  |   .1911853   .0227423     8.41   0.000      .146396    .2359745
            2  |   .0718835   .0147657     4.87   0.000     .0428036    .1009633
            3  |   .1273132   .0103298    12.32   0.000     .1069696    .1476569
            4  |   .0454902    .007626     5.97   0.000     .0304714     .060509
            5  |   .0053776   .0093697     0.57   0.567    -.0130753    .0238306
            6  |   .0725044   .0118869     6.10   0.000     .0490942    .0959147
            7  |   .0703454    .013033     5.40   0.000      .044678    .0960128
            9  |   .0065574   .0142157     0.46   0.645    -.0214393    .0345541
               |
     firstvote |   .1229961    .011103    11.08   0.000     .1011295    .1448627
1.mohighschool |   .1316742   .0073668    17.87   0.000     .1171657    .1461826
       foreign |  -.1434814    .008733   -16.43   0.000    -.1606804   -.1262824
         _cons |   -.034813   .0397042    -0.88   0.381    -.1130074    .0433814
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluster
> (kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(ku
> nta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, clu
> ster(kunta19)

Linear regression                               Number of obs     =     12,363
                                                F(15, 180)        =       8.97
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0088
                                                Root MSE          =     .36797

                                (Std. err. adjusted for 181 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0204226   .0067845     3.01   0.003     .0070352    .0338101
     molincome |   .0081672   .0048791     1.67   0.096    -.0014604    .0177949
        female |     .02917   .0117309     2.49   0.014     .0060222    .0523177
           age |   .0034548   .0012374     2.79   0.006      .001013    .0058966
               |
       moses1d |
            1  |   .1160279    .084573     1.37   0.172    -.0508542      .28291
            2  |    .036888   .0176514     2.09   0.038     .0020577    .0717182
            3  |   .0995803   .0294664     3.38   0.001     .0414363    .1577242
            4  |   .0367726   .0125132     2.94   0.004     .0120812    .0614641
            5  |   .0110196    .010435     1.06   0.292    -.0095711    .0316102
            6  |   .0304552   .0180599     1.69   0.093     -.005181    .0660915
            7  |   .0437504    .021561     2.03   0.044     .0012056    .0862953
            9  |   .0164524   .0177455     0.93   0.355    -.0185636    .0514683
               |
     firstvote |   .0335367   .0230907     1.45   0.148    -.0120266       .0791
1.mohighschool |   .0158714   .0178619     0.89   0.375    -.0193742     .051117
       foreign |  -.0691234   .0120272    -5.75   0.000    -.0928559    -.045391
         _cons |  -.0249575   .0454617    -0.55   0.584    -.1146639    .0647489
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluster
> (kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(k
> unta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginal: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal
> ==1, cluster(kunta19)

Linear regression                               Number of obs     =     24,727
                                                F(15, 228)        =      28.36
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0110
                                                Root MSE          =     .45873

                                (Std. err. adjusted for 229 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0120387   .0046785     2.57   0.011     .0028201    .0212574
     molincome |   .0034205   .0061987     0.55   0.582    -.0087936    .0156346
        female |   .0534637   .0082704     6.46   0.000     .0371675      .06976
           age |   .0063386   .0010389     6.10   0.000     .0042917    .0083856
               |
       moses1d |
            1  |   .1611068   .0308095     5.23   0.000     .1003989    .2218146
            2  |   .0608475   .0203795     2.99   0.003     .0206913    .1010037
            3  |   .0677599   .0130568     5.19   0.000     .0420324    .0934873
            4  |   .0333063   .0126028     2.64   0.009     .0084735    .0581392
            5  |   .0037637   .0126208     0.30   0.766    -.0211047     .028632
            6  |   .0327845   .0198168     1.65   0.099     -.006263     .071832
            7  |   .0463642   .0178321     2.60   0.010     .0112274     .081501
            9  |   .0310556   .0243715     1.27   0.204    -.0169666    .0790777
               |
     firstvote |   .0995251    .018767     5.30   0.000     .0625462    .1365041
1.mohighschool |   .0804653   .0059526    13.52   0.000     .0687362    .0921944
       foreign |  -.1911409   .0200895    -9.51   0.000    -.2307258    -.151556
         _cons |   .0375475   .0627941     0.60   0.550    -.0861836    .1612785
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginal

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginal

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(k
> unta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(ku
> nta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, c
> luster(kunta19)

Linear regression                               Number of obs     =     12,368
                                                F(15, 191)        =      27.95
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0177
                                                Root MSE          =     .49552

                                (Std. err. adjusted for 192 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |  -.0079586   .0077438    -1.03   0.305    -.0232331    .0073158
     molincome |  -.0087864   .0062725    -1.40   0.163    -.0211586    .0035858
        female |   .1021518   .0106052     9.63   0.000     .0812334    .1230702
           age |    .010389   .0015686     6.62   0.000     .0072949     .013483
               |
       moses1d |
            1  |   .0645096   .0362337     1.78   0.077      -.00696    .1359792
            2  |  -.0169443   .0359126    -0.47   0.638    -.0877806     .053892
            3  |   .0639487   .0408966     1.56   0.120    -.0167184    .1446157
            4  |  -.0258092   .0348056    -0.74   0.459    -.0944619    .0428434
            5  |  -.0057295   .0538648    -0.11   0.915    -.1119757    .1005167
            6  |   .0408493   .0411582     0.99   0.322    -.0403338    .1220323
            7  |  -.0267402   .0551935    -0.48   0.629    -.1356073    .0821269
            9  |  -.0936197   .0474362    -1.97   0.050    -.1871858   -.0000536
               |
     firstvote |   .1121405   .0155166     7.23   0.000     .0815347    .1427464
1.mohighschool |   .0495898     .02606     1.90   0.059    -.0018125    .1009921
       foreign |  -.1562625   .2430114    -0.64   0.521    -.6355933    .3230684
         _cons |   .2091908   .0874265     2.39   0.018     .0367452    .3816363
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 `"& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ "'

. local titles2 "& & Bottom 25\% & 25-75\% & Top 25\% \\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. esttab, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prehead(\begin{table}[htbp]\centering ///
> \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} ///
> \caption{Heterogeneity by Vote Propensity} ///
> \begin{tabular}{l*{4}{c}}\hline\hline) posthead("`header'" "`emptyrow'" `"`titles1'"' "`titles2'" "`numbers'" \\
>  \multicolumn{4}{c}{\textbf{Panel A: Direct Effects}} \\ "`emptyrow'")  ///
> refcat(treated "", nolabel below) postfoot(\hline) fragment nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

\begin{table}[htbp]\centering \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} \caption{Heterogeneity by Vote Propensity
> } \begin{tabular}{l*{4}{c}}\hline\hline
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & &  \\ 
& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ 
& & Bottom 25\% & 25-75\% & Top 25\% \\
& (1) & (2) & (3) & (4) \\ \hline
\\
\multicolumn{4}{c}{\textbf{Panel
A:
Direct
Effects}}
\\
& & & &  \\ 
Treated                     0.009***        0.020***        0.012**        -0.008   
                          (0.003)         (0.007)         (0.005)         (0.008)   

                                                                                    
------------------------------------------------------------------------------------
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.309           0.151           0.299           0.485   
Observations               49,458          12,363          24,727          12,368   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                                -0.008         0.020**       -0.028***   
\phantom{se}                              (0.008)         (0.009)         (0.010)   
\hline

. 
. 
. eststo clear

. 
. *Panel B
. *Create voting propensity groups for the spillover effect sample
. sum pvoteold if voted22!=.  & treatedf!=. & female!=., detail

                         Pr(voted22)
-------------------------------------------------------------
      Percentiles      Smallest
 1%      .088845        .025952
 5%     .1792009       .0305654
10%     .2285496       .0320571       Obs              36,723
25%     .3446258       .0320964       Sum of wgt.      36,723

50%     .5015987                      Mean            .498383
                        Largest       Std. dev.      .1964743
75%     .6549283       .9408319
90%     .7655475       .9433452       Variance       .0386021
95%     .8062306       .9439933       Skewness      -.0821849
99%     .8595173       .9450071       Kurtosis       2.121713

. replace marginal=.
(3,282,217 real changes made, 3,282,217 to missing)

. replace marginal=1 if pvoteold>=r(p25) & pvoteold<r(p75)
(1,413,465 real changes made)

. replace marginal=0 if pvoteold<r(p25) | (pvoteold>=r(p75) & pvoteold!=.)
(1,805,722 real changes made)

. 
. replace never=.
(3,282,217 real changes made, 3,282,217 to missing)

. replace never=1 if pvoteold<r(p25)
(836,853 real changes made)

. replace never=0 if pvoteold>=r(p25) & pvoteold!=.
(2,382,334 real changes made)

. 
. replace always=.
(3,282,217 real changes made, 3,282,217 to missing)

. replace always=1 if pvoteold>=r(p75) & pvoteold!=.
(968,869 real changes made)

. replace always=0 if pvoteold<r(p75)
(2,250,318 real changes made)

. 
. 
. eststo all: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal!=.,
>  cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     36,723
                                                F(14, 217)        =     607.06
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1404
                                                Root MSE          =     .46364

                                (Std. err. adjusted for 218 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |    .012563   .0055504     2.26   0.025     .0016233    .0235026
     molincome |   .0250576   .0040537     6.18   0.000     .0170679    .0330472
        female |   .0091824   .0045149     2.03   0.043     .0002837    .0180812
           age |    .008646   .0002746    31.49   0.000     .0081049    .0091871
               |
       moses1d |
            1  |   .2257518   .0207919    10.86   0.000     .1847719    .2667316
            2  |   .0971918   .0168219     5.78   0.000     .0640366     .130347
            3  |   .1776332   .0116406    15.26   0.000     .1546901    .2005764
            4  |   .0870501   .0118255     7.36   0.000     .0637425    .1103577
            5  |   .0032318   .0105084     0.31   0.759    -.0174798    .0239434
            6  |   .1324995   .0163103     8.12   0.000     .1003525    .1646465
            7  |   .0159423   .0174638     0.91   0.362     -.018478    .0503627
            9  |   .0635445    .023615     2.69   0.008     .0170004    .1100887
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1453884   .0079344    18.32   0.000     .1297501    .1610268
       foreign |  -.2465178   .0129547   -19.03   0.000     -.272051   -.2209845
         _cons |  -.2494052    .048748    -5.12   0.000    -.3454853    -.153325
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluste
> r(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(k
> unta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cl
> uster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      9,180
                                                F(14, 184)        =      30.53
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0229
                                                Root MSE          =     .43085

                                (Std. err. adjusted for 185 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0208856   .0104625     2.00   0.047     .0002437    .0415274
     molincome |   .0035272   .0059139     0.60   0.552    -.0081406    .0151951
        female |   .0139607   .0077076     1.81   0.072    -.0012459    .0291672
           age |   .0041081   .0005373     7.65   0.000     .0030482    .0051681
               |
       moses1d |
            1  |    .212147   .1009351     2.10   0.037      .013008     .411286
            2  |   .0715889   .0229785     3.12   0.002     .0262537    .1169241
            3  |   .0719415   .0328179     2.19   0.030     .0071936    .1366893
            4  |   .0455523   .0153033     2.98   0.003     .0153598    .0757448
            5  |  -.0068851   .0151458    -0.45   0.650    -.0367669    .0229967
            6  |   .0110933   .0235374     0.47   0.638    -.0353446    .0575311
            7  |   .0157133   .0223015     0.70   0.482    -.0282863    .0597129
            9  |  -.0073927   .0290275    -0.25   0.799    -.0646622    .0498768
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0855509   .0281185     3.04   0.003     .0300748    .1410269
       foreign |  -.1712977   .0131035   -13.07   0.000      -.19715   -.1454453
         _cons |   .0877604   .0650008     1.35   0.179    -.0404823    .2160032
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluste
> r(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(
> kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginal: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if margina
> l==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     18,362
                                                F(14, 200)        =      23.41
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0246
                                                Root MSE          =       .494

                                (Std. err. adjusted for 201 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |    .016185   .0079057     2.05   0.042     .0005957    .0317742
     molincome |   .0205935   .0072851     2.83   0.005      .006228     .034959
        female |   .0008945   .0086757     0.10   0.918    -.0162131    .0180021
           age |   .0076137   .0004735    16.08   0.000       .00668    .0085474
               |
       moses1d |
            1  |   .1873947   .0331779     5.65   0.000     .1219714     .252818
            2  |   .0744432   .0252671     2.95   0.004     .0246191    .1242672
            3  |   .1525875    .025077     6.08   0.000     .1031383    .2020368
            4  |   .0968502   .0215185     4.50   0.000     .0544179    .1392825
            5  |    .024529   .0154239     1.59   0.113    -.0058853    .0549433
            6  |   .1599369   .0214986     7.44   0.000     .1175438      .20233
            7  |   .0198756   .0259405     0.77   0.444    -.0312763    .0710276
            9  |   .0988988   .0334829     2.95   0.004     .0328739    .1649236
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1288358    .014443     8.92   0.000     .1003556    .1573159
       foreign |  -.2194393   .0430894    -5.09   0.000    -.3044071   -.1344715
         _cons |  -.1614205    .087049    -1.85   0.065    -.3330721    .0102311
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginal

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginal

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(
> kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(k
> unta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, 
> cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      9,181
                                                F(13, 146)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0199
                                                Root MSE          =      .4254

                                (Std. err. adjusted for 147 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |  -.0060862   .0105905    -0.57   0.566    -.0270168    .0148444
     molincome |    .038304   .0094588     4.05   0.000     .0196101    .0569979
        female |   .0086575   .0095668     0.90   0.367    -.0102499    .0275648
           age |   .0045652   .0007605     6.00   0.000     .0030622    .0060682
               |
       moses1d |
            1  |   .1896445   .0653275     2.90   0.004     .0605348    .3187542
            2  |   .1258785   .0671716     1.87   0.063    -.0068757    .2586327
            3  |   .1602729   .0612377     2.62   0.010      .039246    .2812998
            4  |   .0878566   .0593307     1.48   0.141    -.0294015    .2051146
            5  |   .0028248   .0742401     0.04   0.970    -.1438992    .1495488
            6  |   .0935049   .1064861     0.88   0.381    -.1169484    .3039582
            7  |   .0688623   .0691001     1.00   0.321    -.0677034    .2054281
            9  |   .0487897    .098056     0.50   0.620    -.1450029    .2425823
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0583409   .0198528     2.94   0.004     .0191048    .0975769
       foreign |  -.8517415   .0134809   -63.18   0.000    -.8783845   -.8250985
         _cons |  -.0661371    .145343    -0.46   0.650    -.3533852     .221111
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. 
. 
. 
. 
. 
. 
. 
. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{c}
> {\textbf{Panel B: Spillover Effects by HH's Members' Voting Propensity}} \\ "`emptyrow'")  ///
> refcat(treatedf "", nolabel below) postfoot(\hline\hline \\ \end{tabular} \\ \end{table}) fragment append nonote
> s 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& & & &  \\ 
\multicolumn{4}{c}{\textbf{Panel
B:
Spillover
Effects
by
HH's
Members'
Voting
Propensity}}
\\
& & & &  \\ 
Treated in HH               0.013**         0.021**         0.016**        -0.006   
                          (0.006)         (0.010)         (0.008)         (0.011)   

                                                                                    
\hline
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.497           0.242           0.495           0.761   
Observations               36,723           9,180          18,362           9,181   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                                -0.005          0.022*         -0.027*   
\phantom{se}                              (0.013)         (0.013)         (0.015)   
\hline\hline \\ \end{tabular} \\ \end{table}

. 
. *Panel C
. 
. *Generate dummies for individual and hosuehold voting propensity groups
. gen marginalfitemp=marginal if ntreatedf==1 & treated==1
(3,557,501 missing values generated)

. replace marginalfitemp=marginal if ncontrolf==1 & treated==0
(11,635 real changes made)

. bys petu20: egen marginalfi=mean(marginalfitemp)
(3,492,027 missing values generated)

. 
. gen neverfitemp=never if ntreatedf==1 & treated==1
(3,557,501 missing values generated)

. replace neverfitemp=never if ncontrolf==1 & treated==0
(11,635 real changes made)

. bys petu20: egen neverfi=mean(neverfitemp)
(3,492,027 missing values generated)

. 
. 
. gen alwaysfitemp=always if ntreatedf==1 & treated==1
(3,557,501 missing values generated)

. replace alwaysfitemp=always if ncontrolf==1 & treated==0
(11,635 real changes made)

. bys petu20: egen alwaysfi=mean(alwaysfitemp)
(3,492,027 missing values generated)

. 
. 
. 
. eststo clear

. 
. eststo all: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalfi!=
> ., cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     36,373
                                                F(14, 249)        =     572.91
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1405
                                                Root MSE          =     .46361

                                (Std. err. adjusted for 250 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0131569   .0054685     2.41   0.017     .0023866    .0239273
     molincome |   .0249779   .0040463     6.17   0.000     .0170086    .0329472
        female |   .0090503   .0045547     1.99   0.048     .0000797    .0180209
           age |   .0086935   .0002795    31.11   0.000      .008143     .009244
               |
       moses1d |
            1  |   .2296017   .0212343    10.81   0.000       .18778    .2714233
            2  |   .0966819   .0168293     5.74   0.000      .063536    .1298279
            3  |   .1794655   .0117333    15.30   0.000     .1563564    .2025746
            4  |    .087735   .0122545     7.16   0.000     .0635993    .1118706
            5  |   .0044101    .010876     0.41   0.685    -.0170107    .0258309
            6  |   .1330537   .0169863     7.83   0.000     .0995986    .1665088
            7  |   .0193993   .0182036     1.07   0.288    -.0164532    .0552519
            9  |   .0614097   .0234337     2.62   0.009     .0152562    .1075631
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1444895   .0079699    18.13   0.000     .1287925    .1601864
       foreign |  -.2451728   .0138267   -17.73   0.000     -.272405   -.2179406
         _cons |  -.2509818   .0495627    -5.06   0.000    -.3485974   -.1533662
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalfi==1, clus
> ter(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverfi==1, cluster
> (kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverfi==1, 
> cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     20,760
                                                F(14, 229)        =     349.40
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1152
                                                Root MSE          =     .46437

                                (Std. err. adjusted for 230 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0271085   .0082031     3.30   0.001     .0109452    .0432718
     molincome |   .0178498   .0049757     3.59   0.000     .0080458    .0276539
        female |   .0162682   .0054815     2.97   0.003     .0054675    .0270689
           age |   .0089963   .0002929    30.71   0.000     .0084191    .0095735
               |
       moses1d |
            1  |   .1957024   .0322396     6.07   0.000     .1321783    .2592266
            2  |   .0680359   .0205356     3.31   0.001      .027573    .1084988
            3  |   .1458386   .0146772     9.94   0.000      .116919    .1747581
            4  |   .0914388   .0130537     7.00   0.000     .0657181    .1171595
            5  |   .0028563   .0128481     0.22   0.824    -.0224592    .0281718
            6  |    .062552   .0259836     2.41   0.017     .0113544    .1137495
            7  |  -.0020989   .0189233    -0.11   0.912    -.0393849    .0351871
            9  |   .0251113   .0220558     1.14   0.256    -.0183469    .0685694
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1278688   .0102636    12.46   0.000     .1076455     .148092
       foreign |  -.2313824   .0094125   -24.58   0.000    -.2499287   -.2128362
         _cons |  -.2279524   .0523729    -4.35   0.000    -.3311467   -.1247581
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalfi==1, clus
> ter(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysfi==1, cluste
> r(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginalfi: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if margi
> nalfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     15,288
                                                F(14, 202)        =     198.04
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1209
                                                Root MSE          =     .45584

                                (Std. err. adjusted for 203 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |  -.0054758   .0079118    -0.69   0.490    -.0210761    .0101245
     molincome |   .0276413   .0069802     3.96   0.000     .0138779    .0414046
        female |   .0206806   .0071253     2.90   0.004      .006631    .0347302
           age |    .008679   .0004017    21.60   0.000     .0078869    .0094711
               |
       moses1d |
            1  |   .1955698    .030954     6.32   0.000     .1345354    .2566042
            2  |   .0983594   .0214752     4.58   0.000     .0560151    .1407037
            3  |   .1553647   .0237454     6.54   0.000      .108544    .2021853
            4  |   .0622239   .0248811     2.50   0.013     .0131638    .1112839
            5  |   .0057706   .0219725     0.26   0.793    -.0375544    .0490955
            6  |   .1802705   .0296838     6.07   0.000     .1217407    .2388002
            7  |   .0664057   .0295201     2.25   0.026     .0081987    .1246128
            9  |   .1167572   .0430189     2.71   0.007     .0319335     .201581
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0963608   .0107569     8.96   0.000     .0751506    .1175709
       foreign |  -.1537549   .0349182    -4.40   0.000    -.2226058    -.084904
         _cons |  -.1809429   .0829376    -2.18   0.030    -.3444773   -.0174085
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginalfi

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginalfi

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysfi==1, cluste
> r(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverfi==1, cluster
> (kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysfi==1
> , cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =        325
                                                F(14, 55)         =      12.03
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1210
                                                Root MSE          =     .44055

                                 (Std. err. adjusted for 56 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   -.087877   .0539517    -1.63   0.109    -.1959987    .0202446
     molincome |   .0151593   .0406049     0.37   0.710    -.0662147    .0965333
        female |  -.0892747   .0539212    -1.66   0.103    -.1973353    .0187859
           age |   .0061224   .0015812     3.87   0.000     .0029537    .0092911
               |
       moses1d |
            1  |  -.2293201    .173127    -1.32   0.191    -.5762744    .1176342
            2  |  -.0142838   .1522877    -0.09   0.926    -.3194752    .2909076
            3  |  -.1725523   .1565824    -1.10   0.275    -.4863504    .1412458
            4  |  -.1988853    .153575    -1.30   0.201    -.5066564    .1088858
            5  |  -.3216136    .146133    -2.20   0.032    -.6144707   -.0287564
            6  |  -.4685237   .2924531    -1.60   0.115    -1.054613    .1175654
            7  |  -.2768484   .1870723    -1.48   0.145    -.6517497    .0980529
            9  |  -.4322131   .2583897    -1.67   0.100    -.9500376    .0856115
               |
     firstvote |          0  (omitted)
1.mohighschool |    .132013   .0719773     1.83   0.072    -.0122327    .2762587
       foreign |  -.5128455   .1225094    -4.19   0.000    -.7583598   -.2673312
         _cons |   .5081627   .4186683     1.21   0.230    -.3308673    1.347193
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. 
. 
. 
. 
. 
. 
. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 `"& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ "'

. local titles2 "& & Bottom 25\% & 25-75\% & Top 25\% \\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{c}
> {\textbf{Panel C: Spillover Effects by Targeted (Young) Voter's Voting Propensity}} \\ "`emptyrow'")  ///
> refcat(treatedf "", nolabel below) postfoot(\hline\hline \\ \end{tabular} \\ \end{table}) fragment append nonote
> s 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& & & &  \\ 
\multicolumn{4}{c}{\textbf{Panel
C:
Spillover
Effects
by
Targeted
(Young)
Voter's
Voting
Propensity}}
\\
& & & &  \\ 
Treated in HH               0.013**         0.027***       -0.005          -0.088   
                          (0.005)         (0.008)         (0.008)         (0.054)   

                                                                                    
\hline
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.497           0.405           0.620           0.754   
Observations               36,373          20,760          15,288             325   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                             -0.033***           0.082        -0.115**   
\phantom{se}                              (0.011)         (0.055)         (0.055)   
\hline\hline \\ \end{tabular} \\ \end{table}

. 
. 
. 
. 
. 
. *************************************************
. *****TABLE 8
. *************************************************
. *Panel A
. *Create voting propensity groups for the direct effect sample
. 
. sum pvote_enet_mother if voted22!=. & treated!=. & female!=., detail

                   Pr(voted22), penalized
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0995974       .0484223
 5%     .1560467       .0484223
10%     .1748806       .0581356       Obs              50,140
25%     .2101384       .0655169       Sum of wgt.      50,140

50%     .2915984                      Mean           .3078214
                        Largest       Std. dev.      .1102293
75%     .3891061       .7372629
90%     .4602669       .7456779       Variance       .0121505
95%     .5081187       .7522143       Skewness       .3950703
99%     .5738017       .7706261       Kurtosis       2.466242

. replace marginal=.
(3,219,187 real changes made, 3,219,187 to missing)

. replace marginal=1 if pvote_enet_mother>=r(p25) & pvote_enet_mother<r(p75)
(1,973,702 real changes made)

. replace marginal=0 if pvote_enet_mother<r(p25) | (pvote_enet_mother>=r(p75) & pvote_enet_mother!=.)
(1,588,880 real changes made)

. 
. replace never=.
(3,219,187 real changes made, 3,219,187 to missing)

. replace never=1 if pvote_enet_mother<r(p25)
(836,400 real changes made)

. replace never=0 if pvote_enet_mother>=r(p25) & pvote_enet_mother!=.
(2,726,182 real changes made)

. 
. replace always=.
(3,219,187 real changes made, 3,219,187 to missing)

. replace always=1 if pvote_enet_mother>=r(p75) & pvote_enet_mother!=.
(752,480 real changes made)

. replace always=0 if pvote_enet_mother<r(p75)
(2,810,102 real changes made)

. 
. 
. 
. eststo clear

. 
. eststo all: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal!=., 
> cluster(kunta19)

Linear regression                               Number of obs     =     49,679
                                                F(15, 289)        =     303.74
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0622
                                                Root MSE          =     .44939

                                (Std. err. adjusted for 290 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0090149   .0033085     2.72   0.007     .0025032    .0155267
     molincome |   .0026971   .0038687     0.70   0.486    -.0049173    .0103114
        female |   .1044478   .0064625    16.16   0.000     .0917282    .1171674
           age |   .0075646   .0010094     7.49   0.000     .0055779    .0095513
               |
       moses1d |
            1  |   .1897906   .0226286     8.39   0.000     .1452528    .2343283
            2  |   .0721931   .0146617     4.92   0.000     .0433358    .1010504
            3  |   .1282275   .0102864    12.47   0.000     .1079819    .1484732
            4  |    .045854   .0075732     6.05   0.000     .0309485    .0607596
            5  |    .005741    .009352     0.61   0.540    -.0126658    .0241477
            6  |   .0720807   .0117937     6.11   0.000     .0488683    .0952931
            7  |   .0707205   .0129955     5.44   0.000     .0451428    .0962983
            9  |   .0071824   .0140908     0.51   0.611    -.0205513     .034916
               |
     firstvote |   .1234276   .0110779    11.14   0.000      .101624    .1452311
1.mohighschool |   .1319606   .0073446    17.97   0.000     .1175049    .1464162
       foreign |  -.1427179   .0087355   -16.34   0.000    -.1599111   -.1255246
         _cons |  -.0370493   .0395638    -0.94   0.350     -.114919    .0408205
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluster
> (kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(ku
> nta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, clu
> ster(kunta19)

Linear regression                               Number of obs     =     12,361
                                                F(14, 247)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0087
                                                Root MSE          =     .36983

                                (Std. err. adjusted for 248 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0064044   .0062823     1.02   0.309    -.0059692    .0187781
     molincome |   .0107936   .0049726     2.17   0.031     .0009996    .0205877
        female |  -.0033445   .0144156    -0.23   0.817    -.0317377    .0250487
           age |   .0044982   .0012729     3.53   0.000     .0019911    .0070053
               |
       moses1d |
            1  |  -.1436845   .0108644   -13.23   0.000    -.1650833   -.1222858
            2  |   .0522757   .0150125     3.48   0.001     .0227068    .0818446
            3  |   .1169408   .0446073     2.62   0.009     .0290817    .2047999
            4  |   .0389007    .011688     3.33   0.001     .0158798    .0619216
            5  |   .0063027   .0105908     0.60   0.552    -.0145571    .0271626
            6  |   .0379182   .0175561     2.16   0.032     .0033395    .0724969
            7  |   .0351089    .027582     1.27   0.204    -.0192169    .0894348
            9  |   .0228381   .0187834     1.22   0.225    -.0141579    .0598342
               |
     firstvote |   .0795338   .0310253     2.56   0.011     .0184258    .1406418
1.mohighschool |   .0045895   .0188869     0.24   0.808    -.0326104    .0417893
       foreign |  -.0669223    .014135    -4.73   0.000    -.0947628   -.0390819
         _cons |  -.0590963   .0490901    -1.20   0.230    -.1557849    .0375923
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluster
> (kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(k
> unta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginal: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal
> ==1, cluster(kunta19)

Linear regression                               Number of obs     =     24,806
                                                F(15, 275)        =      20.56
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0081
                                                Root MSE          =     .45805

                                (Std. err. adjusted for 276 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0152365   .0046569     3.27   0.001     .0060688    .0244042
     molincome |   .0083926   .0053568     1.57   0.118    -.0021529    .0189381
        female |   .0426949   .0099168     4.31   0.000     .0231724    .0622174
           age |    .005556   .0011825     4.70   0.000      .003228    .0078839
               |
       moses1d |
            1  |   .1762571   .0290284     6.07   0.000      .119111    .2334033
            2  |   .0740638   .0212945     3.48   0.001     .0321428    .1159849
            3  |   .0821713   .0134679     6.10   0.000      .055658    .1086846
            4  |   .0398573   .0119699     3.33   0.001      .016293    .0634215
            5  |   .0146853   .0131719     1.11   0.266    -.0112453    .0406158
            6  |   .0546379   .0203378     2.69   0.008     .0146002    .0946755
            7  |     .05417   .0181562     2.98   0.003     .0184272    .0899128
            9  |   .0185232   .0214146     0.86   0.388    -.0236341    .0606806
               |
     firstvote |   .0832327   .0181953     4.57   0.000      .047413    .1190525
1.mohighschool |   .0664839   .0075298     8.83   0.000     .0516605    .0813073
       foreign |   -.162753   .0194831    -8.35   0.000     -.201108    -.124398
         _cons |    .000141   .0573816     0.00   0.998     -.112822    .1131041
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginal

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginal

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(k
> unta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(ku
> nta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, c
> luster(kunta19)

Linear regression                               Number of obs     =     12,512
                                                F(14, 241)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0149
                                                Root MSE          =      .4962

                                (Std. err. adjusted for 242 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |  -.0015918   .0065727    -0.24   0.809    -.0145392    .0113556
     molincome |  -.0105651   .0050445    -2.09   0.037     -.020502   -.0006282
        female |   .0935378   .0120377     7.77   0.000     .0698254    .1172502
           age |   .0105085   .0015048     6.98   0.000     .0075443    .0134727
               |
       moses1d |
            1  |   .0696583   .0374008     1.86   0.064     -.004016    .1433326
            2  |   .0089856   .0329866     0.27   0.786    -.0559931    .0739644
            3  |   .0825642   .0371578     2.22   0.027     .0093687    .1557597
            4  |  -.0100346   .0290956    -0.34   0.730    -.0673487    .0472796
            5  |   .0016206   .0549311     0.03   0.976    -.1065858    .1098271
            6  |   .0543009    .041876     1.30   0.196    -.0281888    .1367906
            7  |   .0029646   .0427778     0.07   0.945    -.0813015    .0872306
            9  |   -.064645   .0421444    -1.53   0.126    -.1476634    .0183735
               |
     firstvote |   .1186756   .0164094     7.23   0.000     .0863515    .1509997
1.mohighschool |   .0209238   .0314434     0.67   0.506    -.0410153    .0828628
       foreign |   .0919457   .0144967     6.34   0.000     .0633893    .1205021
         _cons |   .2330354   .0712178     3.27   0.001     .0927465    .3733243
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 `"& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ "'

. local titles2 "& & Bottom 25\% & 25-75\% & Top 25\% \\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. esttab, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prehead(\begin{table}[htbp]\centering ///
> \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} ///
> \caption{Heterogeneity by Vote Propensity - Elastic Net} ///
> \begin{tabular}{l*{4}{c}}\hline\hline) posthead("`header'" "`emptyrow'" `"`titles1'"' "`titles2'" "`numbers'" \\
>  \multicolumn{4}{c}{\textbf{Panel A: Direct Effects}} \\ "`emptyrow'")  ///
> refcat(treated "", nolabel below) postfoot(\hline) fragment nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

\begin{table}[htbp]\centering \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} \caption{Heterogeneity by Vote Propensity
>  - Elastic Net} \begin{tabular}{l*{4}{c}}\hline\hline
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & &  \\ 
& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ 
& & Bottom 25\% & 25-75\% & Top 25\% \\
& (1) & (2) & (3) & (4) \\ \hline
\\
\multicolumn{4}{c}{\textbf{Panel
A:
Direct
Effects}}
\\
& & & &  \\ 
Treated                     0.009***        0.006           0.015***       -0.002   
                          (0.003)         (0.006)         (0.005)         (0.007)   

                                                                                    
------------------------------------------------------------------------------------
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.308           0.161           0.294           0.481   
Observations               49,679          12,361          24,806          12,512   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                                 0.009         0.017**          -0.008   
\phantom{se}                              (0.008)         (0.008)         (0.009)   
\hline

. 
. 
. eststo clear

. 
. 
. 
. *Panel B
. *Create voting propensity groups for the spillover effect sample
. 
. sum pvoteold_enet if voted22!=.  & treatedf!=. & female!=., detail

                   Pr(voted22), penalized
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .1156368       .0457579
 5%      .215395       .0514887
10%     .2549483       .0547635       Obs              36,876
25%     .3608298       .0565052       Sum of wgt.      36,876

50%     .4980666                      Mean           .4963368
                        Largest       Std. dev.      .1773194
75%     .6333069       .8794211
90%     .7417891       .8812141       Variance       .0314422
95%     .7798461       .8907157       Skewness      -.0670877
99%     .8239794       .9002653       Kurtosis       2.157478

. replace marginal=.
(3,562,582 real changes made, 3,562,582 to missing)

. replace marginal=1 if pvoteold_enet>=r(p25) & pvoteold_enet<r(p75)
(1,418,317 real changes made)

. replace marginal=0 if pvoteold_enet<r(p25) | (pvoteold_enet>=r(p75) & pvoteold_enet!=.)
(1,933,462 real changes made)

. 
. replace never=.
(3,562,582 real changes made, 3,562,582 to missing)

. replace never=1 if pvoteold_enet<r(p25)
(864,591 real changes made)

. replace never=0 if pvoteold_enet>=r(p25) & pvoteold_enet!=.
(2,487,188 real changes made)

. 
. replace always=.
(3,562,582 real changes made, 3,562,582 to missing)

. replace always=1 if pvoteold_enet>=r(p75) & pvoteold_enet!=.
(1,068,871 real changes made)

. replace always=0 if pvoteold_enet<r(p75)
(2,282,908 real changes made)

. 
. eststo all: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal!=.,
>  cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     36,876
                                                F(14, 259)        =     612.45
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1407
                                                Root MSE          =     .46358

                                (Std. err. adjusted for 260 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0130222   .0055449     2.35   0.020     .0021033     .023941
     molincome |   .0248605   .0040409     6.15   0.000     .0169033    .0328177
        female |   .0095634   .0045088     2.12   0.035     .0006849     .018442
           age |   .0086664   .0002753    31.48   0.000     .0081243    .0092085
               |
       moses1d |
            1  |   .2246265   .0207849    10.81   0.000     .1836976    .2655553
            2  |   .0959936   .0167937     5.72   0.000     .0629242    .1290631
            3  |   .1767397     .01162    15.21   0.000      .153858    .1996215
            4  |   .0857361   .0117858     7.27   0.000     .0625278    .1089443
            5  |   .0027727   .0104861     0.26   0.792    -.0178761    .0234216
            6  |   .1327165   .0162459     8.17   0.000     .1007256    .1647075
            7  |   .0146583   .0174838     0.84   0.403    -.0197702    .0490868
            9  |   .0622016   .0236856     2.63   0.009     .0155608    .1088425
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1455762   .0079108    18.40   0.000     .1299985    .1611539
       foreign |  -.2464759   .0128641   -19.16   0.000    -.2718074   -.2211443
         _cons |  -.2481974   .0485264    -5.11   0.000    -.3437539   -.1526408
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluste
> r(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(k
> unta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cl
> uster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      9,219
                                                F(14, 229)        =      43.52
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0212
                                                Root MSE          =     .43206

                                (Std. err. adjusted for 230 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0130094   .0107731     1.21   0.228    -.0082176    .0342364
     molincome |   .0086678   .0050632     1.71   0.088    -.0013086    .0186442
        female |   .0110969   .0082441     1.35   0.180    -.0051471    .0273409
           age |    .004259    .000498     8.55   0.000     .0032779    .0052402
               |
       moses1d |
            1  |   .1790845   .0823553     2.17   0.031     .0168134    .3413556
            2  |   .0753374   .0214647     3.51   0.001     .0330438    .1176311
            3  |   .0625842   .0427731     1.46   0.145     -.021695    .1468633
            4  |   .0503151   .0149716     3.36   0.001     .0208154    .0798147
            5  |  -.0051956   .0145404    -0.36   0.721    -.0338457    .0234545
            6  |   .0450671   .0270206     1.67   0.097    -.0081737     .098308
            7  |   .0286747   .0218531     1.31   0.191    -.0143842    .0717337
            9  |   .0089216   .0253549     0.35   0.725    -.0410372    .0588805
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1177235   .0290598     4.05   0.000     .0604648    .1749822
       foreign |  -.1766792   .0124009   -14.25   0.000    -.2011137   -.1522447
         _cons |   .0397546   .0533501     0.75   0.457    -.0653652    .1448744
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluste
> r(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(
> kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginal: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if margina
> l==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     18,438
                                                F(14, 234)        =      30.76
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0205
                                                Root MSE          =     .49505

                                (Std. err. adjusted for 235 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |     .01478   .0086299     1.71   0.088    -.0022223    .0317823
     molincome |   .0207388   .0073699     2.81   0.005     .0062189    .0352586
        female |   .0062108    .007943     0.78   0.435    -.0094381    .0218597
           age |   .0082341   .0005397    15.26   0.000     .0071708    .0092973
               |
       moses1d |
            1  |   .2161198   .0302596     7.14   0.000     .1565039    .2757358
            2  |   .0970717   .0214436     4.53   0.000     .0548245    .1393189
            3  |    .179901   .0220769     8.15   0.000     .1364061    .2233959
            4  |   .1130766   .0195979     5.77   0.000     .0744657    .1516876
            5  |    .027415   .0148132     1.85   0.065    -.0017693    .0565994
            6  |   .1846754   .0219463     8.41   0.000     .1414379     .227913
            7  |   .0218525   .0246066     0.89   0.375    -.0266264    .0703313
            9  |   .0924304   .0353319     2.62   0.009     .0228212    .1620396
               |
     firstvote |          0  (omitted)
1.mohighschool |    .124567   .0191753     6.50   0.000     .0867887    .1623454
       foreign |  -.2858157   .0460125    -6.21   0.000    -.3764673   -.1951641
         _cons |  -.2046613   .0835738    -2.45   0.015    -.3693145   -.0400081
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginal

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginal

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(
> kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(k
> unta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, 
> cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      9,219
                                                F(13, 153)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0182
                                                Root MSE          =     .42467

                                (Std. err. adjusted for 154 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0093304   .0118136     0.79   0.431    -.0140084    .0326691
     molincome |   .0400248   .0094788     4.22   0.000     .0212985     .058751
        female |   .0056093   .0092858     0.60   0.547    -.0127357    .0239543
           age |   .0046359   .0009905     4.68   0.000      .002679    .0065928
               |
       moses1d |
            1  |   .1646113   .0722635     2.28   0.024     .0218481    .3073745
            2  |   .0834083   .0774204     1.08   0.283    -.0695426    .2363592
            3  |   .1138805   .0685478     1.66   0.099    -.0215418    .2493028
            4  |   .0336176   .0657501     0.51   0.610    -.0962777    .1635129
            5  |  -.0337417   .0741596    -0.45   0.650    -.1802507    .1127672
            6  |   .0950367   .1066962     0.89   0.374    -.1157512    .3058246
            7  |   .0107123   .0711921     0.15   0.881    -.1299342    .1513588
            9  |    .018723   .0992294     0.19   0.851    -.1773138    .2147597
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0909864   .0148255     6.14   0.000     .0616973    .1202754
       foreign |  -.8624076   .0145594   -59.23   0.000     -.891171   -.8336441
         _cons |  -.0804943   .1684566    -0.48   0.633    -.4132956    .2523071
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. 
. 
. 
. 
. 
. 
. 
. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{c}
> {\textbf{Panel B: Spillover Effects by HH's Members' Voting Propensity}} \\ "`emptyrow'")  ///
> refcat(treatedf "", nolabel below) postfoot(\hline\hline \\ \end{tabular} \\ \end{table}) fragment append nonote
> s 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& & & &  \\ 
\multicolumn{4}{c}{\textbf{Panel
B:
Spillover
Effects
by
HH's
Members'
Voting
Propensity}}
\\
& & & &  \\ 
Treated in HH               0.013**         0.013           0.015*          0.009   
                          (0.006)         (0.011)         (0.009)         (0.012)   

                                                                                    
\hline
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.496           0.247           0.491           0.753   
Observations               36,876           9,219          18,438           9,219   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                                 0.002           0.005          -0.004   
\phantom{se}                              (0.014)         (0.015)         (0.016)   
\hline\hline \\ \end{tabular} \\ \end{table}

. 
. *Panel C
. 
. drop marginalfitemp marginalfi neverfitemp neverfi alwaysfitemp alwaysfi

. 
. 
. gen marginalfitemp=marginal if ntreatedf==1 & treated==1
(3,557,335 missing values generated)

. replace marginalfitemp=marginal if ncontrolf==1 & treated==0
(11,779 real changes made)

. bys petu20: egen marginalfi=mean(marginalfitemp)
(3,491,068 missing values generated)

. 
. gen neverfitemp=never if ntreatedf==1 & treated==1
(3,557,335 missing values generated)

. replace neverfitemp=never if ncontrolf==1 & treated==0
(11,779 real changes made)

. bys petu20: egen neverfi=mean(neverfitemp)
(3,491,068 missing values generated)

. 
. 
. gen alwaysfitemp=always if ntreatedf==1 & treated==1
(3,557,335 missing values generated)

. replace alwaysfitemp=always if ncontrolf==1 & treated==0
(11,779 real changes made)

. bys petu20: egen alwaysfi=mean(alwaysfitemp)
(3,491,068 missing values generated)

. 
. 
. eststo clear

. 
. eststo all: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalfi!=
> ., cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     36,512
                                                F(14, 259)        =     583.06
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1406
                                                Root MSE          =      .4636

                                (Std. err. adjusted for 260 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0134494   .0054366     2.47   0.014     .0027438     .024155
     molincome |   .0249076    .004021     6.19   0.000     .0169896    .0328255
        female |   .0096909   .0045304     2.14   0.033     .0007697     .018612
           age |   .0086994   .0002785    31.24   0.000      .008151    .0092477
               |
       moses1d |
            1  |   .2284349   .0213106    10.72   0.000     .1864709    .2703989
            2  |   .0956238   .0169523     5.64   0.000     .0622418    .1290058
            3  |   .1777949   .0118635    14.99   0.000     .1544337    .2011561
            4  |   .0858704   .0122519     7.01   0.000     .0617443    .1099964
            5  |   .0033106   .0109825     0.30   0.763    -.0183157     .024937
            6  |   .1314337   .0171169     7.68   0.000     .0977276    .1651398
            7  |   .0167998   .0182264     0.92   0.358    -.0190909    .0526905
            9  |   .0606236   .0234022     2.59   0.010     .0145408    .1067063
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1446637   .0079657    18.16   0.000     .1289779    .1603496
       foreign |  -.2453855   .0137194   -17.89   0.000    -.2724014   -.2183697
         _cons |  -.2498583   .0491453    -5.08   0.000    -.3466335   -.1530831
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalfi==1, clus
> ter(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverfi==1, cluster
> (kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverfi==1, 
> cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     20,811
                                                F(14, 245)        =     260.49
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1082
                                                Root MSE          =     .46552

                                (Std. err. adjusted for 246 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0198543   .0081187     2.45   0.015     .0038629    .0358457
     molincome |   .0154388   .0049992     3.09   0.002     .0055919    .0252857
        female |   .0182365   .0052937     3.44   0.001     .0078095    .0286634
           age |   .0089206   .0003043    29.32   0.000     .0083213    .0095199
               |
       moses1d |
            1  |   .1966493   .0305326     6.44   0.000     .1365095    .2567892
            2  |   .0717834   .0217404     3.30   0.001     .0289615    .1146053
            3  |   .1524573   .0151322    10.07   0.000     .1226514    .1822631
            4  |   .0910082   .0134333     6.77   0.000     .0645487    .1174677
            5  |   .0057892   .0136621     0.42   0.672    -.0211211    .0326994
            6  |   .0643595   .0269262     2.39   0.018     .0113232    .1173957
            7  |  -.0037253   .0184521    -0.20   0.840    -.0400703    .0326197
            9  |   .0072996   .0219332     0.33   0.740     -.035902    .0505013
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1156342   .0096954    11.93   0.000     .0965372    .1347311
       foreign |  -.2386339   .0103097   -23.15   0.000    -.2589409   -.2183269
         _cons |  -.1896742   .0536461    -3.54   0.000    -.2953407   -.0840078
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalfi==1, clus
> ter(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysfi==1, cluste
> r(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginalfi: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if margi
> nalfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     15,633
                                                F(14, 217)        =     232.82
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1166
                                                Root MSE          =     .45587

                                (Std. err. adjusted for 218 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0050573   .0089884     0.56   0.574    -.0126584     .022773
     molincome |   .0295055    .006911     4.27   0.000     .0158842    .0431267
        female |   .0145774   .0065023     2.24   0.026     .0017617    .0273931
           age |   .0083741   .0003443    24.32   0.000     .0076954    .0090528
               |
       moses1d |
            1  |   .1989959   .0280849     7.09   0.000     .1436418      .25435
            2  |   .1024297   .0244587     4.19   0.000     .0542227    .1506367
            3  |   .1540056   .0231486     6.65   0.000     .1083807    .1996304
            4  |   .0591198   .0234546     2.52   0.012     .0128918    .1053478
            5  |   -.001166   .0223828    -0.05   0.959    -.0452816    .0429496
            6  |   .1875964     .03243     5.78   0.000     .1236782    .2515146
            7  |   .0634285   .0315319     2.01   0.046     .0012806    .1255764
            9  |   .1402267   .0433374     3.24   0.001     .0548107    .2256427
               |
     firstvote |          0  (omitted)
1.mohighschool |    .105567   .0091207    11.57   0.000     .0875904    .1235436
       foreign |  -.1414924    .036069    -3.92   0.000    -.2125828   -.0704019
         _cons |  -.2013601   .0777462    -2.59   0.010    -.3545944   -.0481258
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginalfi

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginalfi

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysfi==1, cluste
> r(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverfi==1, cluster
> (kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysfi==1
> , cluster(kunta19)
note: firstvote omitted because of collinearity.
note: foreign omitted because of collinearity.

Linear regression                               Number of obs     =         68
                                                F(11, 12)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.2735
                                                Root MSE          =     .41813

                                 (Std. err. adjusted for 13 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |  -.2681908   .1178393    -2.28   0.042    -.5249406   -.0114409
     molincome |  -.0150503   .1034091    -0.15   0.887    -.2403593    .2102587
        female |  -.0783872    .139851    -0.56   0.585    -.3830963    .2263218
           age |   .0047918   .0053543     0.89   0.388    -.0068742    .0164577
               |
       moses1d |
            1  |  -.3453426   .1638384    -2.11   0.057    -.7023158    .0116306
            2  |  -.2481078   .1553218    -1.60   0.136    -.5865249    .0903093
            3  |  -.4622448   .1427668    -3.24   0.007    -.7733069   -.1511827
            4  |  -.3502882   .2227697    -1.57   0.142    -.8356617    .1350852
            5  |  -.5423653   .1586441    -3.42   0.005    -.8880212   -.1967095
            6  |  -.5464732   .3712055    -1.47   0.167     -1.35526    .2623141
            7  |  -.2136296   .1444103    -1.48   0.165    -.5282726    .1010134
               |
     firstvote |          0  (omitted)
1.mohighschool |   .3632606   .1049058     3.46   0.005     .1346904    .5918308
       foreign |          0  (omitted)
         _cons |   1.065192   .9752232     1.09   0.296    -1.059636    3.190021
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. 
. 
. 
. 
. 
. 
. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 `"& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ "'

. local titles2 "& & Bottom 25\% & 25-75\% & Top 25\% \\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{c}
> {\textbf{Panel C: Spillover Effects by Targeted (Young) Voter's Voting Propensity}} \\ "`emptyrow'")  ///
> refcat(treatedf "", nolabel below) postfoot(\hline\hline \\ \end{tabular} \\ \end{table}) fragment append nonote
> s 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& & & &  \\ 
\multicolumn{4}{c}{\textbf{Panel
C:
Spillover
Effects
by
Targeted
(Young)
Voter's
Voting
Propensity}}
\\
& & & &  \\ 
Treated in HH               0.013**         0.020**         0.005          -0.268** 
                          (0.005)         (0.008)         (0.009)         (0.118)   

                                                                                    
\hline
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.496           0.405           0.618           0.867   
Observations               36,512          20,811          15,633              68   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                                -0.015         0.273**        -0.288**   
\phantom{se}                              (0.012)         (0.118)         (0.118)   
\hline\hline \\ \end{tabular} \\ \end{table}

. 
. 
. *************************************************
. *****TABLE 9
. *************************************************
. *Panel A
. 
. eststo clear

. 
. 
. 
. 
. eststo clear

. 
. 
. eststo nohighschool: reg voted22 treated molincome age i.moses1d firstvote female foreign if mohighschool==0, cl
> uster(kunta19)

Linear regression                               Number of obs     =     27,659
                                                F(14, 282)        =      55.09
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0257
                                                Root MSE          =      .4192

                              (Std. err. adjusted for 283 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     treated |   .0088308    .005612     1.57   0.117    -.0022158    .0198775
   molincome |   .0131904   .0043484     3.03   0.003      .004631    .0217498
         age |   .0038212   .0009672     3.95   0.000     .0019173    .0057251
             |
     moses1d |
          1  |   .2171939   .0263293     8.25   0.000     .1653669    .2690208
          2  |   .0749151    .015265     4.91   0.000     .0448673    .1049629
          3  |   .1264926   .0125015    10.12   0.000     .1018844    .1511007
          4  |   .0514738    .009951     5.17   0.000     .0318862    .0710613
          5  |   .0066822   .0097427     0.69   0.493    -.0124954    .0258598
          6  |   .0415574   .0146938     2.83   0.005     .0126339    .0704809
          7  |   .0687645   .0142285     4.83   0.000     .0407569    .0967721
          9  |  -.0073231   .0150974    -0.49   0.628     -.037041    .0223948
             |
   firstvote |   .0967786   .0145609     6.65   0.000     .0681168    .1254404
      female |   .0817663   .0076144    10.74   0.000      .066778    .0967545
     foreign |  -.1162713   .0097106   -11.97   0.000    -.1353858   -.0971569
       _cons |  -.0537793   .0410188    -1.31   0.191    -.1345211    .0269625
------------------------------------------------------------------------------

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store nohighschool

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: nohighschool

. 
. 
. 
. 
. eststo highschool: reg voted22 treated molincome age i.moses1d firstvote female foreign if mohighschool==1, clus
> ter(kunta19)

Linear regression                               Number of obs     =     22,020
                                                F(14, 268)        =     143.25
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0353
                                                Root MSE          =     .48348

                              (Std. err. adjusted for 269 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     treated |   .0093463   .0049048     1.91   0.058    -.0003104    .0190031
   molincome |  -.0068104    .004993    -1.36   0.174    -.0166409    .0030201
         age |   .0104811   .0015037     6.97   0.000     .0075205    .0134417
             |
     moses1d |
          1  |   .1396324   .0371133     3.76   0.000     .0665618     .212703
          2  |   .0579691    .022022     2.63   0.009      .014611    .1013273
          3  |     .12005   .0193885     6.19   0.000     .0818769    .1582231
          4  |   .0241074    .016326     1.48   0.141    -.0080361    .0562508
          5  |  -.0112596   .0227754    -0.49   0.621    -.0561011    .0335818
          6  |   .0852297   .0189974     4.49   0.000     .0478265    .1226329
          7  |     .07467   .0321637     2.32   0.021     .0113443    .1379957
          9  |   .0259994   .0231201     1.12   0.262    -.0195207    .0715196
             |
   firstvote |    .149343   .0155924     9.58   0.000     .1186439     .180042
      female |   .1298418   .0070488    18.42   0.000     .1159637    .1437199
     foreign |  -.2687795    .014686   -18.30   0.000    -.2976942   -.2398648
       _cons |   .1310357   .0609249     2.15   0.032     .0110833     .250988
------------------------------------------------------------------------------

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. mystars diffe stde df

. local dstars2=e(d_stars)

. local sestars2=e(se_stars)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store highschool

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: highschool

. 
. 
. eststo noforeign: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool if  foreign==0, cl
> uster(kunta19)

Linear regression                               Number of obs     =     47,696
                                                F(14, 288)        =     220.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0565
                                                Root MSE          =     .45392

                                (Std. err. adjusted for 289 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0099085   .0033635     2.95   0.003     .0032884    .0165286
     molincome |   .0026558   .0041954     0.63   0.527    -.0056017    .0109134
        female |   .1083853   .0063998    16.94   0.000      .095789    .1209816
           age |   .0078652    .001008     7.80   0.000     .0058813    .0098491
               |
       moses1d |
            1  |   .1889994   .0227342     8.31   0.000     .1442531    .2337457
            2  |   .0725155   .0147866     4.90   0.000      .043412    .1016189
            3  |   .1264622   .0101867    12.41   0.000     .1064124     .146512
            4  |   .0443622   .0077749     5.71   0.000     .0290593    .0596651
            5  |   .0047291   .0092232     0.51   0.609    -.0134243    .0228826
            6  |   .0757086   .0127245     5.95   0.000     .0506637    .1007534
            7  |   .0691745    .013824     5.00   0.000     .0419656    .0963834
            9  |  -.0059386   .0171665    -0.35   0.730    -.0397264    .0278492
               |
     firstvote |   .1240281     .01169    10.61   0.000     .1010195    .1470367
1.mohighschool |   .1338646   .0075685    17.69   0.000      .118968    .1487612
         _cons |  -.0449438   .0426978    -1.05   0.293    -.1289831    .0390956
--------------------------------------------------------------------------------

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store noforeign

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: noforeign

. 
. 
. 
. 
. eststo foreign: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool  if foreign==1, clus
> ter(kunta19)

Linear regression                               Number of obs     =      1,983
                                                F(13, 108)        =       7.94
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0218
                                                Root MSE          =     .31419

                                (Std. err. adjusted for 109 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |  -.0089619   .0130985    -0.68   0.495    -.0349254    .0170016
     molincome |   .0079977   .0061694     1.30   0.198     -.004231    .0202265
        female |   .0093531   .0121841     0.77   0.444    -.0147978    .0335041
           age |  -.0003593     .00227    -0.16   0.875    -.0048588    .0041403
               |
       moses1d |
            2  |   .0518139   .0247764     2.09   0.039     .0027028     .100925
            3  |   .1544569   .0434881     3.55   0.001      .068256    .2406577
            4  |   .0735151   .0203696     3.61   0.000      .033139    .1138912
            5  |   .0051172     .02153     0.24   0.813     -.037559    .0477933
            6  |   .0322543   .0208694     1.55   0.125    -.0091124    .0736211
            7  |   .0954789   .0593012     1.61   0.110    -.0220665    .2130242
            9  |   .0724077   .0297902     2.43   0.017     .0133584    .1314571
               |
     firstvote |   .1056109    .045035     2.35   0.021     .0163437    .1948782
1.mohighschool |   .0204596   .0182969     1.12   0.266     -.015808    .0567272
         _cons |   .0015339   .0623209     0.02   0.980     -.121997    .1250648
--------------------------------------------------------------------------------

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. mystars diffe stde df

. local dstars1=e(d_stars)

. local sestars1=e(se_stars)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store foreign

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: foreign

. 
. 
. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 "& \multicolumn{2}{c}{Educational Background} & \multicolumn{2}{c}{Immigration Background} \\ "

. local titles2 "& No High School & High School & Native & Immigrant\\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. local dstars " "

. local sestars " "

. esttab, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "dstars \hline Differences  & \multico
> lumn{2}{c}{`dstars1'} & \multicolumn{2}{c}{`dstars2'} \\ %" "sestars \phantom{sestars} & \multicolumn{2}{c}{`ses
> tars1'} & \multicolumn{2}{c}{`sestars2'} \\ %") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prehead(\begin{table}[htbp]\centering ///
> \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} ///
> \caption{Heterogeneous Effects by Education and Immigration Background} ///
> \begin{tabular}{l*{4}{c}} \hline\hline) posthead("`header'" "`emptyrow'" `"`titles1'"' "`titles2'" "`numbers'" \
> \ \multicolumn{4}{c}{\textbf{Panel A: Direct Effects}} \\ "`emptyrow'")  ///
> refcat(treated "", nolabel below) postfoot(\hline) fragment nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

\begin{table}[htbp]\centering \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} \caption{Heterogeneous Effects by Educati
> on and Immigration Background} \begin{tabular}{l*{4}{c}} \hline\hline
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & &  \\ 
& \multicolumn{2}{c}{Educational Background} & \multicolumn{2}{c}{Immigration Background} \\ 
& No High School & High School & Native & Immigrant\\
& (1) & (2) & (3) & (4) \\ \hline
\\
\multicolumn{4}{c}{\textbf{Panel
A:
Direct
Effects}}
\\
& & & &  \\ 
Treated                     0.009           0.009*          0.010***       -0.009   
                          (0.006)         (0.005)         (0.003)         (0.013)   

                                                                                    
------------------------------------------------------------------------------------
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.230           0.405           0.316           0.120   
Observations               27,659          22,020          47,696           1,983   
\hline Differ..0~001                                                                
\phantom{sest..01~00                                                                
\hline

. 
. 
. eststo clear

. 
. 
. eststo nohighschool: reg voted22 treatedf molincome age i.moses1d firstvote female foreign if mohighschool==0, c
> luster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     21,811
                                                F(13, 241)        =     137.54
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0989
                                                Root MSE          =     .46827

                              (Std. err. adjusted for 242 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    treatedf |   .0115605   .0079705     1.45   0.148    -.0041402    .0272613
   molincome |   .0337028   .0053767     6.27   0.000     .0231115     .044294
         age |   .0081256   .0003417    23.78   0.000     .0074525    .0087986
             |
     moses1d |
          1  |   .2552667   .0239842    10.64   0.000     .2080213    .3025121
          2  |   .0888696   .0207277     4.29   0.000     .0480389    .1297003
          3  |   .1804387   .0197462     9.14   0.000     .1415415    .2193359
          4  |   .1033966   .0151186     6.84   0.000     .0736152     .133178
          5  |   .0120338   .0133762     0.90   0.369    -.0143154    .0383831
          6  |   .0922208   .0240582     3.83   0.000     .0448297     .139612
          7  |   .0409952    .017917     2.29   0.023     .0057013     .076289
          9  |   .0406829   .0201213     2.02   0.044      .001047    .0803189
             |
   firstvote |          0  (omitted)
      female |   -.001375    .006028    -0.23   0.820    -.0132493    .0104993
     foreign |  -.2286172   .0124767   -18.32   0.000    -.2531946   -.2040399
       _cons |  -.3217853    .063979    -5.03   0.000    -.4478147   -.1957559
------------------------------------------------------------------------------

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store nohighschool

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: nohighschool

. 
. 
. 
. 
. eststo highschool: reg voted22 treatedf molincome age i.moses1d firstvote female foreign if mohighschool==1, clu
> ster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     15,065
                                                F(13, 225)        =     198.26
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1101
                                                Root MSE          =     .45578

                              (Std. err. adjusted for 226 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    treatedf |   .0154095   .0073957     2.08   0.038     .0008357    .0299832
   molincome |   .0100495    .005209     1.93   0.055    -.0002151    .0203142
         age |   .0094282   .0003368    27.99   0.000     .0087645    .0100918
             |
     moses1d |
          1  |   .1432908   .0304292     4.71   0.000     .0833282    .2032535
          2  |   .0952656   .0304237     3.13   0.002     .0353138    .1552174
          3  |   .1500101    .019753     7.59   0.000     .1110857    .1889346
          4  |   .0433286   .0180843     2.40   0.017     .0076923    .0789649
          5  |  -.0440944   .0226017    -1.95   0.052    -.0886324    .0004436
          6  |   .1501195   .0290168     5.17   0.000     .0929401    .2072989
          7  |  -.0512919   .0344929    -1.49   0.138    -.1192624    .0166786
          9  |   .0900749   .0464236     1.94   0.054    -.0014058    .1815555
             |
   firstvote |          0  (omitted)
      female |   .0213682   .0072691     2.94   0.004      .007044    .0356925
     foreign |  -.3231962   .0444445    -7.27   0.000    -.4107769   -.2356155
       _cons |   .0461706    .058421     0.79   0.430    -.0689516    .1612928
------------------------------------------------------------------------------

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. mystars diffe stde df

. local dstars1=e(d_stars)

. local sestars1=e(se_stars)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store highschool

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: highschool

. 
. 
. eststo noforeign: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool if  foreign==0, c
> luster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     35,324
                                                F(13, 259)        =     487.84
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1257
                                                Root MSE          =     .46728

                                (Std. err. adjusted for 260 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0142271   .0057441     2.48   0.014     .0029161    .0255382
     molincome |   .0268136   .0047209     5.68   0.000     .0175174    .0361097
        female |   .0118388   .0044826     2.64   0.009     .0030119    .0206657
           age |   .0088077     .00027    32.62   0.000      .008276    .0093394
               |
       moses1d |
            1  |   .2282214   .0206457    11.05   0.000     .1875666    .2688762
            2  |    .102823   .0171691     5.99   0.000     .0690142    .1366319
            3  |   .1792219   .0118424    15.13   0.000     .1559023    .2025415
            4  |   .0884498   .0120615     7.33   0.000     .0646988    .1122008
            5  |   .0067894   .0109099     0.62   0.534    -.0146939    .0282727
            6  |   .1398662   .0173614     8.06   0.000     .1056788    .1740537
            7  |   .0197255   .0183629     1.07   0.284     -.016434    .0558851
            9  |   .0634803   .0270702     2.35   0.020     .0101746    .1167859
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1464032   .0079507    18.41   0.000     .1307468    .1620595
         _cons |  -.2797463   .0557156    -5.02   0.000    -.3894596    -.170033
--------------------------------------------------------------------------------

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store noforeign

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: noforeign

. 
. 
. 
. 
. eststo foreign: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool  if foreign==1, clu
> ster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      1,552
                                                F(13, 106)        =      13.52
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0586
                                                Root MSE          =     .36342

                                (Std. err. adjusted for 107 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |  -.0173792   .0178894    -0.97   0.334    -.0528468    .0180883
     molincome |    .001537   .0122454     0.13   0.900    -.0227407    .0258146
        female |  -.0276392   .0150833    -1.83   0.070    -.0575433    .0022649
           age |   .0045823   .0005369     8.53   0.000     .0035178    .0056468
               |
       moses1d |
            1  |   .3083931   .2487001     1.24   0.218     -.184679    .8014653
            2  |   .0167796   .0377806     0.44   0.658    -.0581241    .0916833
            3  |   .1496206   .0389642     3.84   0.000     .0723703    .2268709
            4  |   .0877009   .0316636     2.77   0.007     .0249248     .150477
            5  |   -.035806   .0309085    -1.16   0.249    -.0970852    .0254732
            6  |   .0434476    .032371     1.34   0.182    -.0207311    .1076263
            7  |  -.0319359    .039304    -0.81   0.418      -.10986    .0459882
            9  |   .0233613   .0389952     0.60   0.550    -.0539504    .1006731
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0689104    .037871     1.82   0.072    -.0061726    .1439934
         _cons |  -.0297342   .1047235    -0.28   0.777    -.2373588    .1778903
--------------------------------------------------------------------------------

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. mystars diffe stde df

. local dstars2=e(d_stars)

. local sestars2=e(se_stars)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store foreign

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: foreign

. 
. 
. 
. 
. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "dstars \hline Differences  & \multico
> lumn{2}{c}{`dstars1'} & \multicolumn{2}{c}{`dstars2'} \\ %" "sestars \phantom{sestars} & \multicolumn{2}{c}{`ses
> tars1'} & \multicolumn{2}{c}{`sestars2'} \\ %") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{c}
> {\textbf{Panel B: Spillover Effects}} \\ "`emptyrow'")  ///
> refcat(treatedf "", nolabel below) postfoot(\hline\hline \\ \end{tabular} \\ \end{table}) fragment append nonote
> s 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& & & &  \\ 
\multicolumn{4}{c}{\textbf{Panel
B:
Spillover
Effects}}
\\
& & & &  \\ 
Treated in HH               0.012           0.015**         0.014**        -0.017   
                          (0.008)         (0.007)         (0.006)         (0.018)   

                                                                                    
\hline
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.410           0.620           0.510           0.175   
Observations               21,811          15,065          35,324           1,552   
\hline Differ..004~*                                                                
\phantom{sest..01~01                                                                
\hline\hline \\ \end{tabular} \\ \end{table}

. 
. ***********************************************************************************************
. 
. eststo clear

. 
. 
. eststo voted21: reg voted22 treated molincome age i.moses1d firstvote i.mohighschool female foreign if voted21==
> 1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     17,643
                                                F(14, 247)        =     102.14
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0319
                                                Root MSE          =     .48001

                                (Std. err. adjusted for 248 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0278357   .0069734     3.99   0.000     .0141009    .0415706
     molincome |  -.0081015   .0056762    -1.43   0.155    -.0192815    .0030785
           age |   .0133181   .0012263    10.86   0.000     .0109027    .0157335
               |
       moses1d |
            1  |   .0949927   .0332631     2.86   0.005     .0294771    .1605082
            2  |   .0237435   .0184147     1.29   0.198    -.0125264    .0600135
            3  |   .0544834    .017102     3.19   0.002     .0207989    .0881678
            4  |   .0160078   .0136784     1.17   0.243    -.0109334    .0429489
            5  |  -.0215901   .0177279    -1.22   0.224    -.0565073    .0133271
            6  |  -.0006675   .0243762    -0.03   0.978    -.0486792    .0473441
            7  |   .0366777   .0269529     1.36   0.175    -.0164092    .0897646
            9  |  -.0176526   .0223785    -0.79   0.431    -.0617297    .0264245
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0788795   .0073214    10.77   0.000     .0644592    .0932998
        female |   .0792561   .0075087    10.56   0.000     .0644668    .0940453
       foreign |  -.2222125     .02832    -7.85   0.000    -.2779921    -.166433
         _cons |   .2880355   .0627254     4.59   0.000     .1644905    .4115804
--------------------------------------------------------------------------------

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store voted21

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: voted21

. 
. 
. 
. 
. eststo notvoted21: reg voted22 treated molincome age i.moses1d firstvote i.mohighschool female foreign if voted2
> 1==0, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     27,800
                                                F(14, 278)        =      52.06
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0197
                                                Root MSE          =     .31919

                                (Std. err. adjusted for 279 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0060596   .0038754     1.56   0.119    -.0015692    .0136885
     molincome |   .0094144   .0032267     2.92   0.004     .0030627    .0157662
           age |   .0012208   .0007091     1.72   0.086    -.0001751    .0026167
               |
       moses1d |
            1  |   .0903948   .0175428     5.15   0.000     .0558612    .1249284
            2  |   .0410503   .0120947     3.39   0.001     .0172415     .064859
            3  |    .044482   .0108416     4.10   0.000     .0231399    .0658241
            4  |   .0199222   .0062114     3.21   0.001     .0076949    .0321495
            5  |   .0015043   .0063352     0.24   0.812    -.0109668    .0139754
            6  |    .045218   .0095193     4.75   0.000     .0264789    .0639571
            7  |   .0350461   .0099353     3.53   0.000     .0154882     .054604
            9  |  -.0086557   .0123631    -0.70   0.484    -.0329929    .0156815
               |
     firstvote |          0  (omitted)
1.mohighschool |    .045679   .0043396    10.53   0.000     .0371364    .0542217
        female |   .0550365   .0063666     8.64   0.000     .0425037    .0675693
       foreign |  -.0516362   .0066142    -7.81   0.000    -.0646564    -.038616
         _cons |  -.0620882   .0352308    -1.76   0.079    -.1314413    .0072649
--------------------------------------------------------------------------------

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. mystars diffe stde df

. local dstars1=e(d_stars)

. local sestars1=e(se_stars)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store notvoted21

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: notvoted21

. 
. 
. eststo rural: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if  rural==1, 
> cluster(kunta19)

Linear regression                               Number of obs     =      5,335
                                                F(15, 68)         =      39.33
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0520
                                                Root MSE          =     .45428

                                 (Std. err. adjusted for 69 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |    .004578   .0138664     0.33   0.742    -.0230919    .0322479
     molincome |   .0080502   .0101059     0.80   0.428    -.0121158    .0282162
        female |   .1018681   .0158709     6.42   0.000     .0701981     .133538
           age |   .0090139   .0021819     4.13   0.000       .00466    .0133678
               |
       moses1d |
            1  |   .2074894   .0386982     5.36   0.000     .1302684    .2847104
            2  |   .0699109   .0370476     1.89   0.063    -.0040164    .1438382
            3  |   .1325541   .0362238     3.66   0.000     .0602706    .2048376
            4  |    .028214   .0296992     0.95   0.345    -.0310499    .0874778
            5  |   .0089444   .0318538     0.28   0.780    -.0546188    .0725077
            6  |   .0498522   .0506587     0.98   0.329    -.0512356    .1509401
            7  |   .1027897   .0449447     2.29   0.025     .0131041    .1924754
            9  |  -.1208829   .0501498    -2.41   0.019    -.2209552   -.0208106
               |
     firstvote |   .1408842   .0297458     4.74   0.000     .0815273    .2002411
1.mohighschool |   .0942264   .0182472     5.16   0.000     .0578147     .130638
       foreign |  -.2510283   .0237764   -10.56   0.000    -.2984734   -.2035832
         _cons |  -.0770804   .1290603    -0.60   0.552    -.3346163    .1804554
--------------------------------------------------------------------------------

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store rural

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: rural

. 
. 
. 
. 
. eststo urban: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if rural==0, c
> luster(kunta19)

Linear regression                               Number of obs     =     38,791
                                                F(15, 48)         =     414.71
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0649
                                                Root MSE          =     .44833

                                 (Std. err. adjusted for 49 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0116016   .0035413     3.28   0.002     .0044812    .0187219
     molincome |   .0028472   .0046143     0.62   0.540    -.0064305    .0121249
        female |   .1074583   .0080771    13.30   0.000     .0912183    .1236984
           age |    .006726   .0011135     6.04   0.000     .0044873    .0089648
               |
       moses1d |
            1  |   .1667437    .029027     5.74   0.000     .1083809    .2251065
            2  |    .076963   .0166705     4.62   0.000     .0434448    .1104812
            3  |   .1265844   .0114849    11.02   0.000     .1034923    .1496764
            4  |   .0480107   .0079614     6.03   0.000     .0320032    .0640181
            5  |   .0020413    .010244     0.20   0.843    -.0185557    .0226383
            6  |    .068009   .0127618     5.33   0.000     .0423497    .0936682
            7  |   .0701017   .0143323     4.89   0.000     .0412846    .0989188
            9  |   .0222916   .0143728     1.55   0.127    -.0066067      .05119
               |
     firstvote |   .1207779   .0124387     9.71   0.000     .0957682    .1457877
1.mohighschool |   .1388136   .0078761    17.62   0.000     .1229776    .1546497
       foreign |  -.1322875   .0114902   -11.51   0.000    -.1553901    -.109185
         _cons |   -.028877   .0450879    -0.64   0.525    -.1195324    .0617783
--------------------------------------------------------------------------------

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. mystars diffe stde df

. local dstars2=e(d_stars)

. local sestars2=e(se_stars)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store urban

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: urban

. 
. 
. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 "& \multicolumn{2}{c}{Educational Background} & \multicolumn{2}{c}{Immigration Background} \\ "

. local titles2 "& No High School & High School & Native & Immigrant\\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. local dstars " "

. local sestars " "

. esttab, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "dstars \hline Differences  & \multico
> lumn{2}{c}{`dstars1'} & \multicolumn{2}{c}{`dstars2'} \\ %" "sestars \phantom{sestars} & \multicolumn{2}{c}{`ses
> tars1'} & \multicolumn{2}{c}{`sestars2'} \\ %") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prehead(\begin{table}[htbp]\centering ///
> \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} ///
> \caption{Heterogeneous Effects by Voting in 2021 and Municipality Type} ///
> \begin{tabular}{l*{4}{c}} \hline\hline) posthead("`header'" "`emptyrow'" `"`titles1'"' "`titles2'" "`numbers'" \
> \ \multicolumn{4}{c}{\textbf{Panel A: Direct Effects}} \\ "`emptyrow'")  ///
> refcat(treated "", nolabel below) postfoot(\hline) fragment nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

\begin{table}[htbp]\centering \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} \caption{Heterogeneous Effects by Voting 
> in 2021 and Municipality Type} \begin{tabular}{l*{4}{c}} \hline\hline
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & &  \\ 
& \multicolumn{2}{c}{Educational Background} & \multicolumn{2}{c}{Immigration Background} \\ 
& No High School & High School & Native & Immigrant\\
& (1) & (2) & (3) & (4) \\ \hline
\\
\multicolumn{4}{c}{\textbf{Panel
A:
Direct
Effects}}
\\
& & & &  \\ 
Treated                     0.028***        0.006           0.005           0.012***
                          (0.007)         (0.004)         (0.014)         (0.004)   

                                                                                    
------------------------------------------------------------------------------------
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.593           0.114           0.313           0.306   
Observations               17,643          27,800           5,335          38,791   
\hline Differ..02~0.                                                                
\phantom{sest..00~01                                                                
\hline

. 
. *Panel B
. eststo clear

. 
. 
. eststo voted21: reg voted22 treatedf molincome age i.moses1d firstvote i.mohighschool female foreign if voted21=
> =1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     20,646
                                                F(14, 215)        =      76.98
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0634
                                                Root MSE          =     .41587

                                (Std. err. adjusted for 216 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |    .019281   .0067446     2.86   0.005      .005987     .032575
     molincome |   .0125722   .0047795     2.63   0.009     .0031515     .021993
           age |    .005828   .0003036    19.20   0.000     .0052296    .0064263
               |
       moses1d |
            1  |   .1010873   .0241046     4.19   0.000     .0535758    .1485989
            2  |    .032889    .017449     1.88   0.061     -.001504    .0672819
            3  |   .0817658   .0186675     4.38   0.000      .044971    .1185606
            4  |   .0420513   .0150855     2.79   0.006     .0123169    .0717856
            5  |  -.0099453     .01776    -0.56   0.576    -.0449513    .0250606
            6  |   .0688129   .0233498     2.95   0.004     .0227891    .1148367
            7  |    .009147   .0200175     0.46   0.648    -.0303086    .0486026
            9  |  -.0213831   .0340227    -0.63   0.530    -.0884438    .0456776
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0744593   .0084753     8.79   0.000      .057754    .0911647
        female |   .0124232   .0066093     1.88   0.062    -.0006041    .0254506
       foreign |  -.2390881   .0271755    -8.80   0.000    -.2926526   -.1855237
         _cons |   .2783892   .0616012     4.52   0.000     .1569696    .3998089
--------------------------------------------------------------------------------

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store voted21

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: voted21

. 
. 
. 
. 
. eststo notvoted21: reg voted22 treatedf molincome age i.moses1d firstvote i.mohighschool female foreign if voted
> 21==0, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     15,170
                                                F(14, 234)        =      65.97
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0343
                                                Root MSE          =     .36773

                                (Std. err. adjusted for 235 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0073705    .006744     1.09   0.276    -.0059163    .0206572
     molincome |   .0159532   .0053737     2.97   0.003     .0053662    .0265402
           age |   .0025336   .0002446    10.36   0.000     .0020516    .0030155
               |
       moses1d |
            1  |   .0672833   .0296218     2.27   0.024     .0089237    .1256428
            2  |   .0466652   .0146613     3.18   0.002     .0177801    .0755503
            3  |   .0864575   .0130857     6.61   0.000     .0606766    .1122384
            4  |   .0612114    .012741     4.80   0.000     .0361097    .0863131
            5  |   .0014576   .0139157     0.10   0.917    -.0259585    .0288736
            6  |   .0644508   .0155444     4.15   0.000      .033826    .0950756
            7  |  -.0174612   .0152246    -1.15   0.253    -.0474561    .0125336
            9  |   .0569218   .0251857     2.26   0.025      .007302    .1065415
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0533224   .0072564     7.35   0.000     .0390261    .0676186
        female |   .0210135   .0055333     3.80   0.000     .0101121    .0319148
       foreign |  -.0968906   .0064916   -14.93   0.000    -.1096802   -.0841011
         _cons |  -.1502695   .0546483    -2.75   0.006    -.2579349    -.042604
--------------------------------------------------------------------------------

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. mystars diffe stde df

. local dstars1=e(d_stars)

. local sestars1=e(se_stars)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store notvoted21

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: notvoted21

. 
. 
. eststo rural: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if  rural==1,
>  cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      5,599
                                                F(14, 68)         =      66.56
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1192
                                                Root MSE          =     .46758

                                 (Std. err. adjusted for 69 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |    .034459   .0181772     1.90   0.062    -.0018131    .0707311
     molincome |   .0480568   .0105009     4.58   0.000     .0271025     .069011
        female |   .0356037   .0107022     3.33   0.001     .0142478    .0569597
           age |   .0090206    .000575    15.69   0.000     .0078732    .0101681
               |
       moses1d |
            1  |   .1842649   .0427602     4.31   0.000     .0989382    .2695916
            2  |   .0831425   .0402586     2.07   0.043     .0028078    .1634772
            3  |    .175224    .037331     4.69   0.000     .1007312    .2497168
            4  |   .0661173   .0369521     1.79   0.078    -.0076195    .1398542
            5  |  -.0163846   .0321304    -0.51   0.612    -.0804997    .0477305
            6  |   .1247146   .0578056     2.16   0.035     .0093653    .2400639
            7  |   .0098173   .0416712     0.24   0.814    -.0733362    .0929709
            9  |   .0196643    .080887     0.24   0.809    -.1417433    .1810719
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1016921    .021504     4.73   0.000     .0587815    .1446027
       foreign |  -.2816684   .0510728    -5.52   0.000    -.3835825   -.1797542
         _cons |  -.4652117   .0974482    -4.77   0.000    -.6596666   -.2707567
--------------------------------------------------------------------------------

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store rural

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: rural

. 
. 
. 
. 
. eststo urban: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign  if rural==0,
>  cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     29,418
                                                F(14, 48)         =     632.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1445
                                                Root MSE          =     .46258

                                 (Std. err. adjusted for 49 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0111637   .0058345     1.91   0.062    -.0005673    .0228947
     molincome |   .0241405   .0045847     5.27   0.000     .0149224    .0333586
        female |   .0040437   .0051076     0.79   0.432    -.0062259    .0143132
           age |   .0084579   .0003128    27.04   0.000     .0078289    .0090869
               |
       moses1d |
            1  |   .2353514   .0255341     9.22   0.000     .1840117    .2866912
            2  |   .0923665   .0195425     4.73   0.000     .0530737    .1316594
            3  |   .1724684   .0122768    14.05   0.000     .1477843    .1971526
            4  |   .0853245   .0125489     6.80   0.000     .0600932    .1105557
            5  |   .0016051   .0111066     0.14   0.886    -.0207261    .0239364
            6  |   .1193147   .0183408     6.51   0.000     .0824382    .1561913
            7  |   .0146397    .020289     0.72   0.474    -.0261541    .0554336
            9  |   .0738232   .0242713     3.04   0.004     .0250225    .1226239
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1578514   .0084066    18.78   0.000     .1409487     .174754
       foreign |  -.2425728   .0131084   -18.51   0.000    -.2689289   -.2162166
         _cons |   -.238213   .0575594    -4.14   0.000    -.3539439   -.1224821
--------------------------------------------------------------------------------

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. mystars diffe stde df

. local dstars2=e(d_stars)

. local sestars2=e(se_stars)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store urban

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: urban

. 
. 
. 
. 
. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "dstars \hline Differences  & \multico
> lumn{2}{c}{`dstars1'} & \multicolumn{2}{c}{`dstars2'} \\ %" "sestars \phantom{sestars} & \multicolumn{2}{c}{`ses
> tars1'} & \multicolumn{2}{c}{`sestars2'} \\ %") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{c}
> {\textbf{Panel B: Spillover Effects}} \\ "`emptyrow'")  ///
> refcat(treatedf "", nolabel below) postfoot(\hline\hline \\ \end{tabular} \\ \end{table}) fragment append nonote
> s 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& & & &  \\ 
\multicolumn{4}{c}{\textbf{Panel
B:
Spillover
Effects}}
\\
& & & &  \\ 
Treated in HH               0.019***        0.007           0.034*          0.011*  
                          (0.007)         (0.007)         (0.018)         (0.006)   

                                                                                    
\hline
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.745           0.164           0.531           0.496   
Observations               20,646          15,170           5,599          29,418   
\hline Differ..012~}                                                                
\phantom{sest..01~01                                                                
\hline\hline \\ \end{tabular} \\ \end{table}

. 
. 
. 
. *************************************************
. *****TABLE A3
. *************************************************
. *Panel A
. *Create voting propensity groups for the direct effect sample
. 
. eststo clear

. 
. 
. _pctile pvote_mother if voted22!=. & treated!=. & female!=., nquantiles(100)

. 
. 
. replace marginal=.
(3,351,779 real changes made, 3,351,779 to missing)

. replace marginal=1 if pvote_mother>=r(r33) & pvote_mother<r(r67)
(1,088,855 real changes made)

. replace marginal=0 if pvote_mother<r(r33) | (pvote_mother>=r(r67) & pvote_mother!=.)
(2,193,362 real changes made)

. 
. replace never=.
(3,351,779 real changes made, 3,351,779 to missing)

. replace never=1 if pvote_mother<r(r33)
(1,120,831 real changes made)

. replace never=0 if pvote_mother>=r(r33) & pvote_mother!=.
(2,161,386 real changes made)

. 
. replace always=.
(3,351,779 real changes made, 3,351,779 to missing)

. replace always=1 if pvote_mother>=r(r67) & pvote_mother!=.
(1,072,531 real changes made)

. replace always=0 if pvote_mother<r(r67)
(2,209,686 real changes made)

. 
. 
. eststo clear

. 
. eststo all: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal!=., 
> cluster(kunta19)

Linear regression                               Number of obs     =     49,458
                                                F(15, 252)        =     304.85
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0622
                                                Root MSE          =     .44966

                                (Std. err. adjusted for 253 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0087153    .003324     2.62   0.009     .0021689    .0152617
     molincome |   .0026628   .0038812     0.69   0.493    -.0049809    .0103066
        female |   .1048359   .0064817    16.17   0.000     .0920706    .1176012
           age |   .0075299   .0010074     7.47   0.000     .0055459     .009514
               |
       moses1d |
            1  |   .1911853   .0227423     8.41   0.000      .146396    .2359745
            2  |   .0718835   .0147657     4.87   0.000     .0428036    .1009633
            3  |   .1273132   .0103298    12.32   0.000     .1069696    .1476569
            4  |   .0454902    .007626     5.97   0.000     .0304714     .060509
            5  |   .0053776   .0093697     0.57   0.567    -.0130753    .0238306
            6  |   .0725044   .0118869     6.10   0.000     .0490942    .0959147
            7  |   .0703454    .013033     5.40   0.000      .044678    .0960128
            9  |   .0065574   .0142157     0.46   0.645    -.0214393    .0345541
               |
     firstvote |   .1229961    .011103    11.08   0.000     .1011295    .1448627
1.mohighschool |   .1316742   .0073668    17.87   0.000     .1171657    .1461826
       foreign |  -.1434814    .008733   -16.43   0.000    -.1606804   -.1262824
         _cons |   -.034813   .0397042    -0.88   0.381    -.1130074    .0433814
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluster
> (kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(ku
> nta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, clu
> ster(kunta19)

Linear regression                               Number of obs     =     16,321
                                                F(15, 189)        =      18.47
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0095
                                                Root MSE          =     .38148

                                (Std. err. adjusted for 190 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0216763   .0067791     3.20   0.002     .0083039    .0350488
     molincome |   .0071033   .0046358     1.53   0.127    -.0020412    .0162478
        female |   .0211364   .0093776     2.25   0.025     .0026382    .0396347
           age |   .0045356   .0009896     4.58   0.000     .0025835    .0064877
               |
       moses1d |
            1  |   .0856466   .0625613     1.37   0.173    -.0377615    .2090546
            2  |    .041238    .017899     2.30   0.022     .0059305    .0765455
            3  |   .0753855   .0220503     3.42   0.001     .0318893    .1188818
            4  |   .0420071    .013262     3.17   0.002     .0158466    .0681677
            5  |     .01217    .009805     1.24   0.216    -.0071712    .0315112
            6  |   .0338064   .0189913     1.78   0.077    -.0036558    .0712686
            7  |   .0427675   .0171053     2.50   0.013     .0090257    .0765094
            9  |   .0107481   .0159133     0.68   0.500    -.0206425    .0421386
               |
     firstvote |    .052533   .0288255     1.82   0.070    -.0043281    .1093941
1.mohighschool |   .0418772   .0162014     2.58   0.010     .0099185     .073836
       foreign |  -.0796663   .0103039    -7.73   0.000    -.0999917   -.0593409
         _cons |  -.0284541   .0457745    -0.62   0.535    -.1187486    .0618403
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluster
> (kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(k
> unta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginal: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal
> ==1, cluster(kunta19)

Linear regression                               Number of obs     =     16,814
                                                F(15, 214)        =      12.61
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0051
                                                Root MSE          =     .45918

                                (Std. err. adjusted for 215 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0100586   .0047006     2.14   0.033     .0007932     .019324
     molincome |   .0018834       .006     0.31   0.754    -.0099432    .0137101
        female |   .0393499   .0128723     3.06   0.003     .0139773    .0647226
           age |   .0050232   .0015751     3.19   0.002     .0019186    .0081279
               |
       moses1d |
            1  |   .1401735   .0351298     3.99   0.000     .0709288    .2094183
            2  |   .0424977   .0229471     1.85   0.065    -.0027337     .087729
            3  |    .059709   .0172847     3.45   0.001     .0256389    .0937791
            4  |   .0218476   .0163259     1.34   0.182    -.0103326    .0540278
            5  |    .007106   .0140701     0.51   0.614    -.0206277    .0348396
            6  |   .0097074   .0209058     0.46   0.643    -.0315002     .050915
            7  |   .0408282   .0228438     1.79   0.075    -.0041996    .0858559
            9  |   .0316203   .0291692     1.08   0.280    -.0258755    .0891161
               |
     firstvote |   .0811356    .022423     3.62   0.000     .0369373    .1253338
1.mohighschool |   .0541826   .0079504     6.82   0.000     .0385115    .0698537
       foreign |  -.1625756   .0317856    -5.11   0.000    -.2252286   -.0999227
         _cons |   .1052659   .0703091     1.50   0.136    -.0333211    .2438529
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginal

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginal

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(k
> unta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(ku
> nta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, c
> luster(kunta19)

Linear regression                               Number of obs     =     16,323
                                                F(15, 209)        =      31.58
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0168
                                                Root MSE          =     .49444

                                (Std. err. adjusted for 210 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |  -.0055367   .0067628    -0.82   0.414    -.0188687    .0077954
     molincome |  -.0055242   .0053588    -1.03   0.304    -.0160884    .0050401
        female |   .0934968   .0094796     9.86   0.000      .074809    .1121846
           age |   .0097715   .0013519     7.23   0.000     .0071063    .0124366
               |
       moses1d |
            1  |   .0803938   .0328707     2.45   0.015     .0155933    .1451944
            2  |  -.0011533   .0428683    -0.03   0.979     -.085663    .0833564
            3  |   .0556617   .0361822     1.54   0.125    -.0156672    .1269906
            4  |  -.0225327   .0296587    -0.76   0.448    -.0810012    .0359358
            5  |  -.0316895    .041783    -0.76   0.449    -.1140596    .0506806
            6  |    .035898   .0419826     0.86   0.393    -.0468657    .1186617
            7  |   -.026063   .0407672    -0.64   0.523    -.1064306    .0543045
            9  |  -.0128525   .0573724    -0.22   0.823    -.1259553    .1002503
               |
     firstvote |   .1110505    .016015     6.93   0.000     .0794788    .1426222
1.mohighschool |   .0738455   .0231463     3.19   0.002     .0282153    .1194757
       foreign |  -.2246848   .1347315    -1.67   0.097    -.4902917    .0409221
         _cons |   .1653186   .0784345     2.11   0.036     .0106944    .3199429
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 `"& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ "'

. local titles2 "& & Bottom 33\% & 33-67\% & Top 33\% \\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. 
. esttab, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prehead(\begin{table}[htbp]\centering ///
> \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} ///
> \caption{Heterogeneity by Thirds of Vote Propensity} ///
> \begin{tabular}{l*{4}{c}}\hline\hline) posthead("`header'" "`emptyrow'" `"`titles1'"' "`titles2'" "`numbers'" \\
>  \multicolumn{4}{c}{\textbf{Panel A: Direct Effects}} \\ "`emptyrow'")  ///
> refcat(treated "", nolabel below) postfoot(\hline) fragment nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

\begin{table}[htbp]\centering \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} \caption{Heterogeneity by Thirds of Vote 
> Propensity} \begin{tabular}{l*{4}{c}}\hline\hline
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & &  \\ 
& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ 
& & Bottom 33\% & 33-67\% & Top 33\% \\
& (1) & (2) & (3) & (4) \\ \hline
\\
\multicolumn{4}{c}{\textbf{Panel
A:
Direct
Effects}}
\\
& & & &  \\ 
Treated                     0.009***        0.022***        0.010**        -0.006   
                          (0.003)         (0.007)         (0.005)         (0.007)   

                                                                                    
------------------------------------------------------------------------------------
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.309           0.166           0.298           0.463   
Observations               49,458          16,321          16,814          16,323   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                                -0.012          0.016*       -0.027***   
\phantom{se}                              (0.008)         (0.008)         (0.010)   
\hline

. 
. *Panel B
. *Create voting propensity groups for the spillover effect sample
. 
. eststo clear

. 
. _pctile pvoteold if voted22!=. & treatedf!=. & female!=., nquantiles(100)

. 
. 
. replace marginal=.
(3,282,217 real changes made, 3,282,217 to missing)

. replace marginal=1 if pvoteold>=r(r33) & pvoteold<r(r67)
(891,722 real changes made)

. replace marginal=0 if pvoteold<r(r33) | (pvoteold>=r(r67) & pvoteold!=.)
(2,327,465 real changes made)

. 
. replace never=.
(3,282,217 real changes made, 3,282,217 to missing)

. replace never=1 if pvoteold<r(r33)
(1,055,465 real changes made)

. replace never=0 if pvoteold>=r(r33) & pvoteold!=.
(2,163,722 real changes made)

. 
. replace always=.
(3,282,217 real changes made, 3,282,217 to missing)

. replace always=1 if pvoteold>=r(r67) & pvoteold!=.
(1,272,000 real changes made)

. replace always=0 if pvoteold<r(r67)
(1,947,187 real changes made)

. 
. 
. 
. eststo all: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal!=.,
>  cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     36,723
                                                F(14, 217)        =     607.06
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1404
                                                Root MSE          =     .46364

                                (Std. err. adjusted for 218 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |    .012563   .0055504     2.26   0.025     .0016233    .0235026
     molincome |   .0250576   .0040537     6.18   0.000     .0170679    .0330472
        female |   .0091824   .0045149     2.03   0.043     .0002837    .0180812
           age |    .008646   .0002746    31.49   0.000     .0081049    .0091871
               |
       moses1d |
            1  |   .2257518   .0207919    10.86   0.000     .1847719    .2667316
            2  |   .0971918   .0168219     5.78   0.000     .0640366     .130347
            3  |   .1776332   .0116406    15.26   0.000     .1546901    .2005764
            4  |   .0870501   .0118255     7.36   0.000     .0637425    .1103577
            5  |   .0032318   .0105084     0.31   0.759    -.0174798    .0239434
            6  |   .1324995   .0163103     8.12   0.000     .1003525    .1646465
            7  |   .0159423   .0174638     0.91   0.362     -.018478    .0503627
            9  |   .0635445    .023615     2.69   0.008     .0170004    .1100887
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1453884   .0079344    18.32   0.000     .1297501    .1610268
       foreign |  -.2465178   .0129547   -19.03   0.000     -.272051   -.2209845
         _cons |  -.2494052    .048748    -5.12   0.000    -.3454853    -.153325
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluste
> r(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(k
> unta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cl
> uster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     12,118
                                                F(14, 192)        =      40.63
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0302
                                                Root MSE          =     .44545

                                (Std. err. adjusted for 193 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0252253   .0081637     3.09   0.002     .0091232    .0413274
     molincome |   .0069801   .0048234     1.45   0.149    -.0025336    .0164937
        female |   .0180231   .0065327     2.76   0.006      .005138    .0309082
           age |   .0051908   .0005716     9.08   0.000     .0040633    .0063184
               |
       moses1d |
            1  |   .1182005   .0645447     1.83   0.069    -.0091073    .2455083
            2  |   .0660558    .020997     3.15   0.002     .0246413    .1074702
            3  |    .097597    .023381     4.17   0.000     .0514804    .1437136
            4  |   .0494491   .0141048     3.51   0.001     .0216288    .0772694
            5  |   .0101896   .0160711     0.63   0.527    -.0215091    .0418882
            6  |   .0530467   .0257161     2.06   0.040     .0023243    .1037692
            7  |   .0292219    .019199     1.52   0.130    -.0086462      .06709
            9  |    .021806   .0254008     0.86   0.392    -.0282945    .0719065
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1094643   .0156423     7.00   0.000     .0786115    .1403172
       foreign |  -.1932214   .0129397   -14.93   0.000    -.2187436   -.1676992
         _cons |   .0190174   .0546125     0.35   0.728    -.0887001     .126735
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluste
> r(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(
> kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginal: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if margina
> l==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     12,486
                                                F(14, 188)        =      12.36
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0115
                                                Root MSE          =     .49741

                                (Std. err. adjusted for 189 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0140736   .0081657     1.72   0.086    -.0020346    .0301817
     molincome |   .0132485      .0091     1.46   0.147    -.0047027    .0311997
        female |   -.011274   .0090436    -1.25   0.214     -.029114    .0065661
           age |    .006094   .0006159     9.89   0.000     .0048791    .0073089
               |
       moses1d |
            1  |   .1893606   .0399403     4.74   0.000     .1105719    .2681493
            2  |   .0536888   .0330188     1.63   0.106    -.0114461    .1188237
            3  |   .1211833   .0341585     3.55   0.000     .0538002    .1885664
            4  |   .0872229   .0255761     3.41   0.001     .0367699    .1376759
            5  |   .0127282   .0202592     0.63   0.531    -.0272365    .0526928
            6  |   .1423322   .0321762     4.42   0.000     .0788595    .2058049
            7  |  -.0003232   .0315486    -0.01   0.992     -.062558    .0619117
            9  |    .092715   .0440238     2.11   0.037     .0058709    .1795592
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0909813   .0198918     4.57   0.000     .0517414    .1302212
       foreign |  -.1729173   .0537972    -3.21   0.002    -.2790411   -.0667935
         _cons |    .011493   .1007005     0.11   0.909    -.1871551    .2101411
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginal

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginal

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(
> kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(k
> unta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, 
> cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     12,119
                                                F(14, 160)        =      17.24
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0286
                                                Root MSE          =     .44036

                                (Std. err. adjusted for 161 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   -.003166    .009791    -0.32   0.747    -.0225022    .0161702
     molincome |   .0381911    .007172     5.33   0.000     .0240271    .0523552
        female |   .0071155    .007662     0.93   0.354    -.0080162    .0222472
           age |   .0059796   .0006307     9.48   0.000      .004734    .0072251
               |
       moses1d |
            1  |   .1663515   .0489725     3.40   0.001     .0696357    .2630674
            2  |   .0830621   .0469336     1.77   0.079    -.0096272    .1757514
            3  |   .1323483   .0426065     3.11   0.002     .0482047    .2164919
            4  |   .0614397    .042811     1.44   0.153    -.0231077    .1459872
            5  |  -.0156084   .0506731    -0.31   0.758    -.1156827    .0844659
            6  |   .0736684   .0600187     1.23   0.221    -.0448627    .1921995
            7  |   .0328302   .0558996     0.59   0.558    -.0775659    .1432264
            9  |   .0082821   .0713423     0.12   0.908    -.1326119    .1491761
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0944779   .0154266     6.12   0.000     .0640119    .1249439
       foreign |  -.3706993   .3565942    -1.04   0.300    -1.074938    .3335392
         _cons |  -.1489463   .1096075    -1.36   0.176    -.3654103    .0675177
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. 
. 
. 
. 
. 
. 
. 
. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{c}
> {\textbf{Panel B: Spillover Effects}} \\ "`emptyrow'")  ///
> refcat(treatedf "", nolabel below) postfoot(\hline\hline \\ \end{tabular} \\ \end{table}) fragment append nonote
> s 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& & & &  \\ 
\multicolumn{4}{c}{\textbf{Panel
B:
Spillover
Effects}}
\\
& & & &  \\ 
Treated in HH               0.013**         0.025***        0.014*         -0.003   
                          (0.006)         (0.008)         (0.008)         (0.010)   

                                                                                    
\hline
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.497           0.272           0.494           0.727   
Observations               36,723          12,118          12,486          12,119   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                                -0.011           0.017        -0.028**   
\phantom{se}                              (0.012)         (0.013)         (0.013)   
\hline\hline \\ \end{tabular} \\ \end{table}

. 
. *************************************************
. *****TABLE A4
. *************************************************
. *Panel A
. *Create voting propensity groups for the direct effect sample
. 
. 
. 
. _pctile pvote_enet_mother if voted22!=.  & treated!=. & female!=., nquantiles(100)

. 
. 
. replace marginal=.
(3,219,187 real changes made, 3,219,187 to missing)

. replace marginal=1 if pvote_enet_mother>=r(r33) & pvote_enet_mother<r(r67)
(1,330,613 real changes made)

. replace marginal=0 if pvote_enet_mother<r(r33) | (pvote_enet_mother>=r(r67) & pvote_enet_mother!=.)
(2,231,969 real changes made)

. 
. replace never=.
(3,219,187 real changes made, 3,219,187 to missing)

. replace never=1 if pvote_enet_mother<r(r33)
(1,246,615 real changes made)

. replace never=0 if pvote_enet_mother>=r(r33) & pvote_enet_mother!=.
(2,315,967 real changes made)

. 
. replace always=.
(3,219,187 real changes made, 3,219,187 to missing)

. replace always=1 if pvote_enet_mother>=r(r67) & pvote_enet_mother!=.
(985,354 real changes made)

. replace always=0 if pvote_enet_mother<r(r67)
(2,577,228 real changes made)

. 
. 
. 
. eststo clear

. 
. eststo all: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal!=., 
> cluster(kunta19)

Linear regression                               Number of obs     =     49,679
                                                F(15, 289)        =     303.74
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0622
                                                Root MSE          =     .44939

                                (Std. err. adjusted for 290 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0090149   .0033085     2.72   0.007     .0025032    .0155267
     molincome |   .0026971   .0038687     0.70   0.486    -.0049173    .0103114
        female |   .1044478   .0064625    16.16   0.000     .0917282    .1171674
           age |   .0075646   .0010094     7.49   0.000     .0055779    .0095513
               |
       moses1d |
            1  |   .1897906   .0226286     8.39   0.000     .1452528    .2343283
            2  |   .0721931   .0146617     4.92   0.000     .0433358    .1010504
            3  |   .1282275   .0102864    12.47   0.000     .1079819    .1484732
            4  |    .045854   .0075732     6.05   0.000     .0309485    .0607596
            5  |    .005741    .009352     0.61   0.540    -.0126658    .0241477
            6  |   .0720807   .0117937     6.11   0.000     .0488683    .0952931
            7  |   .0707205   .0129955     5.44   0.000     .0451428    .0962983
            9  |   .0071824   .0140908     0.51   0.611    -.0205513     .034916
               |
     firstvote |   .1234276   .0110779    11.14   0.000      .101624    .1452311
1.mohighschool |   .1319606   .0073446    17.97   0.000     .1175049    .1464162
       foreign |  -.1427179   .0087355   -16.34   0.000    -.1599111   -.1255246
         _cons |  -.0370493   .0395638    -0.94   0.350     -.114919    .0408205
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluster
> (kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(ku
> nta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, clu
> ster(kunta19)

Linear regression                               Number of obs     =     16,266
                                                F(15, 254)        =      22.76
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0093
                                                Root MSE          =     .38396

                                (Std. err. adjusted for 255 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |    .008763   .0074287     1.18   0.239    -.0058668    .0233928
     molincome |    .007709   .0041488     1.86   0.064    -.0004615    .0158795
        female |   .0091195   .0112371     0.81   0.418    -.0130102    .0312492
           age |   .0050914   .0010541     4.83   0.000     .0030155    .0071673
               |
       moses1d |
            1  |   .0499824   .0964043     0.52   0.605    -.1398713     .239836
            2  |    .068013   .0190475     3.57   0.000     .0305018    .1055242
            3  |   .0775109   .0260511     2.98   0.003     .0262072    .1288147
            4  |   .0484116   .0128059     3.78   0.000     .0231925    .0736308
            5  |   .0130514   .0097591     1.34   0.182    -.0061677    .0322705
            6  |   .0417826   .0188374     2.22   0.027     .0046852      .07888
            7  |   .0603102   .0146626     4.11   0.000     .0314345    .0891859
            9  |   .0134434    .017074     0.79   0.432    -.0201812     .047068
               |
     firstvote |   .0755652    .026586     2.84   0.005     .0232081    .1279222
1.mohighschool |  -.0099313   .0164984    -0.60   0.548    -.0424225    .0225598
       foreign |  -.0777411   .0104128    -7.47   0.000    -.0982475   -.0572347
         _cons |  -.0369582   .0400394    -0.92   0.357    -.1158096    .0418932
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluster
> (kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(k
> unta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginal: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal
> ==1, cluster(kunta19)

Linear regression                               Number of obs     =     16,718
                                                F(15, 272)        =       6.97
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0039
                                                Root MSE          =     .45697

                                (Std. err. adjusted for 273 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0085313   .0055194     1.55   0.123    -.0023349    .0193975
     molincome |   .0079194   .0066962     1.18   0.238    -.0052635    .0211022
        female |   .0030361   .0173716     0.17   0.861    -.0311639    .0372361
           age |   .0052438   .0015537     3.38   0.001     .0021851    .0083026
               |
       moses1d |
            1  |   .1304367   .0335474     3.89   0.000     .0643911    .1964823
            2  |    .048041   .0227168     2.11   0.035     .0033179    .0927641
            3  |   .0419737   .0187929     2.23   0.026     .0049757    .0789716
            4  |    .014095   .0165193     0.85   0.394     -.018427     .046617
            5  |  -.0005587   .0146952    -0.04   0.970    -.0294894    .0283721
            6  |   .0281491   .0241466     1.17   0.245    -.0193889    .0756871
            7  |   .0570747   .0255284     2.24   0.026     .0068163    .1073331
            9  |   .0008065   .0306629     0.03   0.979    -.0595601    .0611732
               |
     firstvote |   .0637763   .0222511     2.87   0.004     .0199701    .1075824
1.mohighschool |    .019715   .0154077     1.28   0.202    -.0106185    .0500484
       foreign |  -.1466436   .0348104    -4.21   0.000    -.2151757   -.0781115
         _cons |   .0712558   .0795632     0.90   0.371    -.0853822    .2278938
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginal

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginal

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(k
> unta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(ku
> nta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, c
> luster(kunta19)

Linear regression                               Number of obs     =     16,695
                                                F(14, 257)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0154
                                                Root MSE          =     .49458

                                (Std. err. adjusted for 258 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |    .009066   .0058558     1.55   0.123    -.0024654    .0205974
     molincome |  -.0050913   .0052487    -0.97   0.333    -.0154272    .0052445
        female |   .1016191    .009471    10.73   0.000     .0829684    .1202698
           age |   .0094464   .0013273     7.12   0.000     .0068327    .0120601
               |
       moses1d |
            1  |   .1057423    .030723     3.44   0.001     .0452413    .1662432
            2  |   .0409511   .0273354     1.50   0.135    -.0128787    .0947809
            3  |   .0989305   .0269066     3.68   0.000     .0459449    .1519161
            4  |   .0199494   .0211892     0.94   0.347    -.0217772    .0616761
            5  |   .0019502   .0369055     0.05   0.958    -.0707256    .0746259
            6  |   .0780191   .0340491     2.29   0.023     .0109684    .1450698
            7  |    .039635   .0449943     0.88   0.379    -.0489695    .1282395
            9  |   .0165497   .0458582     0.36   0.718    -.0737561    .1068555
               |
     firstvote |   .1125494   .0157046     7.17   0.000     .0816233    .1434755
1.mohighschool |    .072186   .0271355     2.66   0.008     .0187498    .1256223
       foreign |   .1023799   .0154588     6.62   0.000     .0719378     .132822
         _cons |    .106466   .0742633     1.43   0.153     -.039776    .2527081
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 `"& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ "'

. local titles2 "& & Bottom 33\% & 33-67\% & Top 33\% \\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. esttab, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prehead(\begin{table}[htbp]\centering ///
> \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} ///
> \caption{Heterogeneity by Thirds of Vote Propensity - Elastic Net} ///
> \begin{tabular}{l*{4}{c}}\hline\hline) posthead("`header'" "`emptyrow'" `"`titles1'"' "`titles2'" "`numbers'" \\
>  \multicolumn{4}{c}{\textbf{Panel A: Direct Effects}} \\ "`emptyrow'")  ///
> refcat(treated "", nolabel below) postfoot(\hline) fragment nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

\begin{table}[htbp]\centering \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} \caption{Heterogeneity by Thirds of Vote 
> Propensity - Elastic Net} \begin{tabular}{l*{4}{c}}\hline\hline
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & &  \\ 
& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ 
& & Bottom 33\% & 33-67\% & Top 33\% \\
& (1) & (2) & (3) & (4) \\ \hline
\\
\multicolumn{4}{c}{\textbf{Panel
A:
Direct
Effects}}
\\
& & & &  \\ 
Treated                     0.009***        0.009           0.009           0.009   
                          (0.003)         (0.007)         (0.006)         (0.006)   

                                                                                    
------------------------------------------------------------------------------------
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.308           0.176           0.294           0.451   
Observations               49,679          16,266          16,718          16,695   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                                -0.000          -0.001           0.000   
\phantom{se}                              (0.009)         (0.008)         (0.009)   
\hline

. 
. 
. *Panel B
. *Create voting propensity groups for the spillover effect sample
. 
. eststo clear

. 
. 
. 
. _pctile pvoteold_enet if voted22!=.  & treatedf!=. & female!=., nquantiles(100)

. 
. 
. replace marginal=.
(3,562,582 real changes made, 3,562,582 to missing)

. replace marginal=1 if pvoteold_enet>=r(r33) & pvoteold_enet<r(r67)
(842,133 real changes made)

. replace marginal=0 if pvoteold_enet<r(r33) | (pvoteold_enet>=r(r67) & pvoteold_enet!=.)
(2,509,646 real changes made)

. 
. replace never=.
(3,562,582 real changes made, 3,562,582 to missing)

. replace never=1 if pvoteold_enet<r(r33)
(1,092,431 real changes made)

. replace never=0 if pvoteold_enet>=r(r33) & pvoteold_enet!=.
(2,259,348 real changes made)

. 
. replace always=.
(3,562,582 real changes made, 3,562,582 to missing)

. replace always=1 if pvoteold_enet>=r(r67) & pvoteold_enet!=.
(1,417,215 real changes made)

. replace always=0 if pvoteold_enet<r(r67)
(1,934,564 real changes made)

. 
. 
. eststo all: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal!=.,
>  cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     36,876
                                                F(14, 259)        =     612.45
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1407
                                                Root MSE          =     .46358

                                (Std. err. adjusted for 260 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0130222   .0055449     2.35   0.020     .0021033     .023941
     molincome |   .0248605   .0040409     6.15   0.000     .0169033    .0328177
        female |   .0095634   .0045088     2.12   0.035     .0006849     .018442
           age |   .0086664   .0002753    31.48   0.000     .0081243    .0092085
               |
       moses1d |
            1  |   .2246265   .0207849    10.81   0.000     .1836976    .2655553
            2  |   .0959936   .0167937     5.72   0.000     .0629242    .1290631
            3  |   .1767397     .01162    15.21   0.000      .153858    .1996215
            4  |   .0857361   .0117858     7.27   0.000     .0625278    .1089443
            5  |   .0027727   .0104861     0.26   0.792    -.0178761    .0234216
            6  |   .1327165   .0162459     8.17   0.000     .1007256    .1647075
            7  |   .0146583   .0174838     0.84   0.403    -.0197702    .0490868
            9  |   .0622016   .0236856     2.63   0.009     .0155608    .1088425
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1455762   .0079108    18.40   0.000     .1299985    .1611539
       foreign |  -.2464759   .0128641   -19.16   0.000    -.2718074   -.2211443
         _cons |  -.2481974   .0485264    -5.11   0.000    -.3437539   -.1526408
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluste
> r(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(k
> unta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cl
> uster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     12,169
                                                F(14, 239)        =      37.78
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0264
                                                Root MSE          =      .4456

                                (Std. err. adjusted for 240 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0128813   .0085219     1.51   0.132    -.0039063    .0296689
     molincome |   .0095685   .0048645     1.97   0.050    -.0000142    .0191513
        female |   .0201258   .0076647     2.63   0.009     .0050267    .0352248
           age |   .0048483   .0005345     9.07   0.000     .0037953    .0059013
               |
       moses1d |
            1  |    .157094   .0486176     3.23   0.001     .0613203    .2528676
            2  |   .0818499   .0219164     3.73   0.000     .0386758     .125024
            3  |   .1028051   .0291757     3.52   0.001     .0453307    .1602795
            4  |   .0480507   .0147774     3.25   0.001     .0189401    .0771612
            5  |   .0131738   .0159333     0.83   0.409    -.0182138    .0445614
            6  |   .0608353   .0226739     2.68   0.008     .0161691    .1055015
            7  |   .0180271   .0203154     0.89   0.376     -.021993    .0580471
            9  |    .003158    .027488     0.11   0.909    -.0509917    .0573077
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1172811   .0196514     5.97   0.000     .0785691    .1559931
       foreign |  -.1900088   .0144499   -13.15   0.000    -.2184742   -.1615433
         _cons |   .0102972   .0537787     0.19   0.848    -.0956435     .116238
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1, cluste
> r(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(
> kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginal: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if margina
> l==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     12,537
                                                F(14, 215)        =       8.85
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0081
                                                Root MSE          =     .49824

                                (Std. err. adjusted for 216 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |    .016632    .008367     1.99   0.048     .0001402    .0331238
     molincome |   .0155659   .0088072     1.77   0.079    -.0017936    .0329253
        female |  -.0014523   .0087442    -0.17   0.868    -.0186877    .0157831
           age |   .0064885   .0010461     6.20   0.000     .0044265    .0085505
               |
       moses1d |
            1  |   .1898845   .0460415     4.12   0.000     .0991341     .280635
            2  |   .0786072   .0309686     2.54   0.012     .0175663    .1396481
            3  |   .1640322   .0339703     4.83   0.000     .0970747    .2309896
            4  |   .1085391   .0257313     4.22   0.000     .0578212    .1592571
            5  |    .028119   .0196233     1.43   0.153    -.0105597    .0667976
            6  |   .1826845   .0329746     5.54   0.000     .1176895    .2476794
            7  |   .0171458   .0278506     0.62   0.539    -.0377493     .072041
            9  |   .1218483   .0428898     2.84   0.005     .0373099    .2063867
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0801371   .0290265     2.76   0.006     .0229241    .1373501
       foreign |  -.2986576   .0522362    -5.72   0.000    -.4016182    -.195697
         _cons |  -.0526682   .1108428    -0.48   0.635     -.271146    .1658095
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginal

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginal

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, cluster(
> kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if never==1, cluster(k
> unta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if always==1, 
> cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     12,170
                                                F(14, 163)        =      19.53
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0290
                                                Root MSE          =     .44109

                                (Std. err. adjusted for 164 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0072103   .0103375     0.70   0.486    -.0132024     .027623
     molincome |   .0383195   .0075046     5.11   0.000     .0235007    .0531382
        female |   .0017229   .0073164     0.24   0.814    -.0127243    .0161702
           age |   .0063981   .0006969     9.18   0.000     .0050219    .0077742
               |
       moses1d |
            1  |   .1801989   .0408771     4.41   0.000      .099482    .2609158
            2  |   .0725519   .0477387     1.52   0.131    -.0217142     .166818
            3  |   .1245395   .0384685     3.24   0.001     .0485787    .2005003
            4  |   .0492944   .0389272     1.27   0.207    -.0275722    .1261611
            5  |  -.0559861   .0488954    -1.15   0.254    -.1525362     .040564
            6  |   .0776829   .0703401     1.10   0.271    -.0612125    .2165782
            7  |   .0076778   .0497666     0.15   0.878    -.0905925     .105948
            9  |  -.0014014   .0666199    -0.02   0.983    -.1329507    .1301479
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1205373    .016104     7.48   0.000     .0887379    .1523368
       foreign |  -.1025818   .2259238    -0.45   0.650    -.5486965     .343533
         _cons |  -.1923029   .0998053    -1.93   0.056    -.3893809    .0047751
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. 
. 
. 
. 
. 
. 
. 
. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{c}
> {\textbf{Panel B: Spillover Effects}} \\ "`emptyrow'")  ///
> refcat(treatedf "", nolabel below) postfoot(\hline\hline \\ \end{tabular} \\ \end{table}) fragment append nonote
> s 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& & & &  \\ 
\multicolumn{4}{c}{\textbf{Panel
B:
Spillover
Effects}}
\\
& & & &  \\ 
Treated in HH               0.013**         0.013           0.017**         0.007   
                          (0.006)         (0.009)         (0.008)         (0.010)   

                                                                                    
\hline
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.496           0.277           0.494           0.720   
Observations               36,876          12,169          12,537          12,170   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                                 0.004           0.009          -0.006   
\phantom{se}                              (0.012)         (0.013)         (0.013)   
\hline\hline \\ \end{tabular} \\ \end{table}

. 
. 
. 
. 
. 
end of do-file

. do "$home\dofiles\Table6_and_A11-12.do"

. ****************************************************************************************************************
> ****
. *****Do-file for Table 6 and Tables A11-12
. *****Who is mobilized to vote by short text messages? Evidence from a nationwide field experiment with young vot
> ers
. ****************************************************************************************************************
> ****
. *****Last edited 24/6/5
. ****************************************************************************************************************
> ****
. *****Ado packages needed: estout
. ****************************************************************************************************************
> ****
. 
. 
. clear all

. 
. *Programs to calculate group differences with standard errors and stars
. capture program drop, myrepost

. program myrepost, eclass
  1. ereturn repost b=`1'
  2. ereturn repost V=`2'
  3. ereturn scalar df_r=`3'
  4. end

. 
. capture program drop mystars

. program mystars, eclass
  1. local d_stars=string(`1', "%9.3f")
  2. local pval=ttail(`3',abs(`1'/`2'))*2
  3. 
. if `pval'<=0.01 {
  4. local d_stars="`d_stars'"+"***"         
  5. }
  6. 
. if `pval'>0.01 & `pval'<=0.05  {
  7. local d_stars="`d_stars'"+"**"          
  8. }
  9. 
. if `pval'>0.05 & `pval'<=0.1  {
 10. local d_stars="`d_stars'"+"*"           
 11. }
 12. 
. local se_stars=string(`2', "%9.3f")
 13. 
. local se_stars="("+"`se_stars'"+")"
 14. 
. ereturn local d_stars="`d_stars'"
 15. ereturn local se_stars="`se_stars'"
 16. 
. 
. end

. 
. *Use data
. use \data\dataforanalysis230522_v2.dta, clear

. *Set basecategory
. fvset base 8 moses1d

. 
. 
. gen treatedfa=1 if ntreatedf>0 & treated==. &  ntreatedf!=.
(3,538,897 missing values generated)

. replace treatedfa=0 if ncontrolf>0 & treated==. & ntreatedf<1 & ncontrolf!=. 
(21,625 real changes made)

. 
. gen totpottreat=ntreatedf+ncontrolf
(1,236,347 missing values generated)

. bys petu20: gen fiid=_n

. 
. tab totpottreat if treatedfa!=.

totpottreat |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |     52,672       92.39       92.39
          2 |      4,018        7.05       99.44
          3 |        298        0.52       99.96
          4 |         19        0.03       99.99
          5 |          2        0.00       99.99
          6 |          3        0.01      100.00
------------+-----------------------------------
      Total |     57,012      100.00

. tab totpottreat if treatedfa!=. & fiid==1

totpottreat |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |     16,458       94.82       94.82
          2 |        861        4.96       99.78
          3 |         34        0.20       99.98
          4 |          2        0.01       99.99
          5 |          1        0.01       99.99
          6 |          1        0.01      100.00
------------+-----------------------------------
      Total |     17,357      100.00

. 
. reg voted22 treatedfa molincome age i.moses1d firstvote i.mohighschool female foreign, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     39,591
                                                F(14, 260)        =     708.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1400
                                                Root MSE          =     .46362

                                (Std. err. adjusted for 261 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
     treatedfa |   .0172992   .0056627     3.05   0.002     .0061485    .0284498
     molincome |   .0260659   .0040839     6.38   0.000     .0180241    .0341076
           age |   .0086937   .0002512    34.61   0.000     .0081991    .0091882
               |
       moses1d |
            1  |   .2173106   .0180812    12.02   0.000     .1817064    .2529149
            2  |    .096513   .0174168     5.54   0.000      .062217    .1308091
            3  |   .1709361   .0116294    14.70   0.000     .1480362     .193836
            4  |   .0807024   .0115969     6.96   0.000     .0578665    .1035382
            5  |   -.001663   .0101538    -0.16   0.870    -.0216573    .0183312
            6  |    .125026   .0185422     6.74   0.000     .0885139     .161538
            7  |   .0074647   .0163892     0.46   0.649    -.0248079    .0397372
            9  |   .0515681   .0249661     2.07   0.040     .0024065    .1007297
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1431021   .0082257    17.40   0.000     .1269047    .1592996
        female |   .0104618   .0045223     2.31   0.021     .0015568    .0193667
       foreign |  -.2516252   .0115378   -21.81   0.000    -.2743445   -.2289059
         _cons |  -.2555092   .0483536    -5.28   0.000    -.3507238   -.1602947
--------------------------------------------------------------------------------

. 
. 
. gen treatedf_i=1 if ntreatedf==1 & treated==1
(3,557,163 missing values generated)

. replace treatedf_i=0 if ncontrolf==1 & treated==0
(11,908 real changes made)

. 
. gen notsamehh=1 if treatedf14!=. & treatedf==.
(3,506,590 missing values generated)

. replace treatedf=treatedf14 if notsamehh==1
(67,694 real changes made)

. gen old=1 if age>49 & age!=.
(2,014,290 missing values generated)

. replace old=0 if age<50 & age!=.
(2,002,588 real changes made)

. 
. label variable treatedf_i "Treatments Pooled"

. label variable treatedf "Spillover Treatment"

. label variable treated1 "Neutral Treatment"

. label variable treated2 "Expressive Treatment"

. label variable treated3 "Informative Treatment"

. 
. 
. *************************************************
. *****TABLE A11
. *************************************************
. 
. eststo clear

. eststo M1: reg voted22 treatedf_i, cluster(kunta19)

Linear regression                               Number of obs     =     28,564
                                                F(1, 276)         =       1.51
                                                Prob > F          =     0.2196
                                                R-squared         =     0.0000
                                                Root MSE          =     .46793

                              (Std. err. adjusted for 277 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  treatedf_i |   .0061224   .0049762     1.23   0.220    -.0036737    .0159184
       _cons |   .3201878   .0116099    27.58   0.000     .2973325     .343043
------------------------------------------------------------------------------

. estadd local Controls "No"

added macro:
           e(Controls) : "No"

. estadd local Controls1 " "

added macro:
          e(Controls1) : " "

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedf_i==0

Mean estimation                         Number of obs = 11,715

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3201878   .0043107      .3117382    .3286374
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M1

. eststo M2: reg voted22 treatedf_i molincome female age foreign, cluster(kunta19)

Linear regression                               Number of obs     =     28,302
                                                F(5, 276)         =     250.32
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0207
                                                Root MSE          =     .46341

                              (Std. err. adjusted for 277 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  treatedf_i |   .0056772   .0050794     1.12   0.265    -.0043221    .0156765
   molincome |   .0232302   .0036782     6.32   0.000     .0159893     .030471
      female |   .0982342   .0069792    14.08   0.000     .0844949    .1119734
         age |   .0036373   .0014731     2.47   0.014     .0007373    .0065373
     foreign |  -.2221962   .0101993   -21.79   0.000    -.2422746   -.2021179
       _cons |  -.0246068   .0426682    -0.58   0.565    -.1086033    .0593897
------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income"

added macro:
          e(Controls1) : "Ln Income"

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedf_i==0

Mean estimation                         Number of obs = 11,604

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |     .32101   .0043342      .3125143    .3295057
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M2

. eststo M3:  reg voted22 treatedf_i molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(kunt
> a19)

Linear regression                               Number of obs     =     28,302
                                                F(15, 276)        =     266.99
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0626
                                                Root MSE          =     .45347

                                (Std. err. adjusted for 277 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
    treatedf_i |   .0055496   .0050279     1.10   0.271    -.0043483    .0154476
     molincome |    .001289   .0045961     0.28   0.779    -.0077589     .010337
        female |   .0860287    .006721    12.80   0.000     .0727978    .0992597
           age |   .0087571   .0011262     7.78   0.000       .00654    .0109742
       foreign |  -.1629302   .0089584   -18.19   0.000    -.1805657   -.1452948
               |
       moses1d |
            1  |   .1919952   .0278197     6.90   0.000     .1372295    .2467609
            2  |   .0689762   .0190055     3.63   0.000     .0315621    .1063902
            3  |   .1379027   .0148355     9.30   0.000     .1086975    .1671079
            4  |   .0512725   .0128439     3.99   0.000      .025988     .076557
            5  |   .0094929   .0133621     0.71   0.478    -.0168117    .0357974
            6  |   .0811331   .0152783     5.31   0.000     .0510564    .1112099
            7  |   .0908696   .0189987     4.78   0.000     .0534687    .1282704
            9  |    .032599   .0272889     1.19   0.233     -.021122    .0863199
               |
     firstvote |   .1156634   .0122891     9.41   0.000     .0914711    .1398556
1.mohighschool |   .1393067   .0073969    18.83   0.000     .1247451    .1538682
         _cons |  -.0376865   .0455577    -0.83   0.409    -.1273713    .0519982
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedf_i==0

Mean estimation                         Number of obs = 11,604

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |     .32101   .0043342      .3125143    .3295057
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M3

. eststo M4:  reg voted22 treatedf_i i.kunta19, cluster(kunta19)

Linear regression                               Number of obs     =     28,564
                                                F(0, 276)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0238
                                                Root MSE          =     .46459

                              (Std. err. adjusted for 277 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  treatedf_i |   .0059595   .0050704     1.18   0.241     -.004022     .015941
             |
     kunta19 |
          9  |   .2613748   .0002277  1148.00   0.000     .2609266     .261823
         10  |   .0073887   .0001723    42.88   0.000     .0070495    .0077279
         18  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
         19  |    .043665   .0000433  1008.62   0.000     .0435797    .0437502
         20  |  -.1075003   .0002544  -422.57   0.000    -.1080011   -.1069995
         43  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
         47  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
         49  |   .0415091   .0002842   146.04   0.000     .0409496    .0420686
         50  |   .2109524   .0004849   435.07   0.000     .2099979    .2119069
         51  |   .0033117   .0002585    12.81   0.000     .0028028    .0038207
         52  |   .7079727   .0020503   345.30   0.000     .7039364    .7120089
         60  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
         61  |   .0440871   .0003159   139.58   0.000     .0434653    .0447089
         69  |   .0462396   .0000308  1500.88   0.000      .046179    .0463003
         71  |   .0579902   .0002572   225.50   0.000     .0574839    .0584964
         72  |   .0103565   .0000222   467.23   0.000     .0103128    .0104001
         74  |   .1315346   .0000846  1555.17   0.000     .1313681    .1317011
         75  |  -.2905375   .0007827  -371.19   0.000    -.2920783   -.2889966
         77  |  -.0348814   .0001622  -215.04   0.000    -.0352007    -.034562
         78  |  -.2880543   .0013299  -216.59   0.000    -.2906724   -.2854362
         79  |  -.1157823   .0000663 -1747.51   0.000    -.1159127   -.1156519
         81  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
         82  |  -.2860678     .00302   -94.72   0.000    -.2920131   -.2801226
         86  |  -.0179209    .000078  -229.79   0.000    -.0180744   -.0177674
         90  |  -.2890476   .0004849  -596.13   0.000    -.2900021   -.2880931
         91  |   .0914194   .0000719  1271.99   0.000     .0912779    .0915609
         92  |  -.0139686   .0000553  -252.40   0.000    -.0140775   -.0138596
         98  |  -.0375577   .0017525   -21.43   0.000    -.0410076   -.0341078
         99  |    .045279   .0013299    34.05   0.000     .0426609    .0478971
        102  |   .0442857   .0004849    91.34   0.000     .0433312    .0452403
        103  |   .7079727   .0020503   345.30   0.000     .7039364    .7120089
        105  |  -.2890476   .0004849  -596.13   0.000    -.2900021   -.2880931
        106  |   .0744533   .0003696   201.42   0.000     .0737257     .075181
        108  |  -.0026508   .0000693   -38.27   0.000    -.0027872   -.0025145
        109  |    .059347    .000408   145.44   0.000     .0585437    .0601502
        111  |   .0442857   .0004849    91.34   0.000     .0433312    .0452403
        139  |   .1774204   .0003159   561.71   0.000     .1767986    .1780422
        140  |  -.1349722   .0006799  -198.52   0.000    -.1363106   -.1336338
        143  |   .1390982   .0001227  1133.63   0.000     .1388566    .1393397
        145  |  -.0046103   .0006016    -7.66   0.000    -.0057947    -.003426
        146  |  -.1149415   .0002757  -416.93   0.000    -.1154842   -.1143988
        148  |  -.2894733   .0001227 -2359.16   0.000    -.2897148   -.2892317
        149  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        151  |   .1389279   .0000222  6267.76   0.000     .1388843    .1389715
        152  |  -.0420273   .0020503   -20.50   0.000    -.0460636   -.0379911
        153  |   .0866974   .0011187    77.50   0.000     .0844952    .0888996
        165  |  -.0502042   .0001271  -394.86   0.000    -.0504545   -.0499539
        167  |    .008787   .0000327   268.78   0.000     .0087226    .0088514
        169  |  -.0127205   .0000222  -573.89   0.000    -.0127641   -.0126768
        170  |   .7079727   .0020503   345.30   0.000     .7039364    .7120089
        171  |  -.0360678     .00302   -11.94   0.000    -.0420131   -.0301226
        172  |  -.2880543   .0013299  -216.59   0.000    -.2906724   -.2854362
        176  |   .0422992   .0012052    35.10   0.000     .0399266    .0446719
        177  |  -.0896435   .0000222 -4044.28   0.000    -.0896872   -.0895999
        178  |   .2099592   .0003602   582.91   0.000     .2092501    .2106682
        179  |   .0662865    .000059  1123.05   0.000     .0661703    .0664027
        181  |  -.2890476   .0004849  -596.13   0.000    -.2900021   -.2880931
        182  |  -.0821424   .0001345  -610.82   0.000    -.0824071   -.0818776
        186  |  -.0392604   .0003038  -129.24   0.000    -.0398585   -.0386624
        202  |   .0113736   .0000172   660.65   0.000     .0113397    .0114075
        204  |   .3091646   .0010362   298.35   0.000     .3071246    .3112045
        205  |  -.0715267   .0005951  -120.19   0.000    -.0726982   -.0703552
        208  |  -.1640476   .0004849  -338.33   0.000    -.1650021   -.1630931
        211  |   .0130701    .000308    42.44   0.000     .0124638    .0136764
        213  |  -.2900408   .0003602  -805.25   0.000    -.2907499   -.2893318
        214  |  -.0046103   .0006016    -7.66   0.000    -.0057947    -.003426
        216  |  -.0747619   .0004849  -154.19   0.000    -.0757164   -.0738074
        217  |   .1002373   .0001236   811.16   0.000      .099994    .1004805
        218  |  -.2890476   .0004849  -596.13   0.000    -.2900021   -.2880931
        224  |  -.0103967   .0003745   -27.76   0.000    -.0111339   -.0096594
        226  |   .0739928   .0000222  3338.20   0.000     .0739492    .0740365
        230  |   .1509019    .000559   269.94   0.000     .1498014    .1520024
        231  |   -.017133   .0002065   -82.95   0.000    -.0175396   -.0167264
        232  |  -.0664943   .0007666   -86.74   0.000    -.0680033   -.0649852
        233  |   .0188739   .0006799    27.76   0.000     .0175355    .0202124
        236  |   .0561532    .000078   720.01   0.000     .0559997    .0563067
        239  |   .1108332   .0003835   289.03   0.000     .1100783    .1115881
        240  |   .0009071   .0003141     2.89   0.004     .0002888    .0015255
        241  |  -.1984094   .0002544  -779.91   0.000    -.1989102   -.1979086
        244  |   .0999482   6.34e-06  1.6e+04   0.000     .0999357    .0999607
        245  |  -.0042358   .0000239  -177.02   0.000    -.0042829   -.0041887
        249  |  -.2884516   .0009919  -290.81   0.000    -.2904043    -.286499
        250  |    .041306   .0020503    20.15   0.000     .0372698    .0453422
        256  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        257  |  -.0080611   .0001509   -53.42   0.000    -.0083581   -.0077641
        260  |  -.0020563   .0015714    -1.31   0.192    -.0051497    .0010371
        261  |   .0184155   .0002899    63.53   0.000     .0178449    .0189861
        263  |  -.1474675   .0006016  -245.11   0.000    -.1486519   -.1462831
        265  |  -.0405375   .0007827   -51.79   0.000    -.0420783   -.0389966
        271  |   .1103565   .0000222  4978.75   0.000     .1103128    .1104001
        272  |   .0184155   .0002899    63.53   0.000     .0178449    .0189861
        273  |  -.0390476   .0004849   -80.53   0.000    -.0400021   -.0380931
        275  |   .2109524   .0004849   435.07   0.000     .2099979    .2119069
        276  |   .0236745   .0001769   133.86   0.000     .0233263    .0240227
        280  |   .7099592   .0003602  1971.07   0.000     .7092501    .7106682
        285  |   .0002177   .0004147     0.52   0.600    -.0005987    .0010341
        286  |   .0605054   .0001046   578.48   0.000     .0602995    .0607114
        287  |   .1891052   5.90e-07  3.2e+05   0.000     .1891041    .1891064
        288  |  -.0405375   .0007827   -51.79   0.000    -.0420783   -.0389966
        290  |   .3746393   .0020503   182.72   0.000     .3706031    .3786755
        291  |   .7099592   .0003602  1971.07   0.000     .7092501    .7106682
        297  |   .1327247   .0002755   481.68   0.000     .1321823    .1332672
        300  |  -.0017706   .0002456    -7.21   0.000    -.0022541   -.0012871
        301  |   .0939633   .0008802   106.75   0.000     .0922305    .0956961
        304  |   .1091646   .0010362   105.35   0.000     .1071246    .1112045
        305  |  -.0952856   .0000163 -5851.95   0.000    -.0953176   -.0952535
        309  |  -.1551195   .0001443 -1074.80   0.000    -.1554036   -.1548354
        312  |  -.2890476   .0004849  -596.13   0.000    -.2900021   -.2880931
        317  |  -.2905375   .0007827  -371.19   0.000    -.2920783   -.2889966
        320  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        322  |  -.0762307   .0000454 -1677.64   0.000    -.0763202   -.0761413
        398  |  -.0039482   .0000383  -103.19   0.000    -.0040235   -.0038729
        399  |  -.1457648    .000847  -172.09   0.000    -.1474322   -.1440973
        400  |  -.2886219    .000847  -340.74   0.000    -.2902894   -.2869544
        402  |  -.0880251   .0001378  -638.76   0.000    -.0882964   -.0877538
        403  |   .1477075   .0001489   991.83   0.000     .1474143    .1480006
        405  |    .145735   .0004849   300.57   0.000     .1447805    .1466895
        407  |  -.2890476   .0004849  -596.13   0.000    -.2900021   -.2880931
        408  |  -.0155077   .0011763   -13.18   0.000    -.0178233    -.013192
        410  |    .024771   .0000378   654.50   0.000     .0246965    .0248455
        416  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        418  |   .0947274   .0002302   411.54   0.000     .0942742    .0951805
        420  |    .045279   .0013299    34.05   0.000     .0426609    .0478971
        422  |  -.0622083   .0001161  -535.74   0.000    -.0624369   -.0619797
        423  |   .3115484   .0009919   314.09   0.000     .3095957     .313501
        425  |    .091681   .0000412  2224.51   0.000     .0915998    .0917621
        426  |  -.0126106   .0003108   -40.57   0.000    -.0132225   -.0119987
        430  |   .0866974   .0011187    77.50   0.000     .0844952    .0888996
        433  |  -.1457648    .000847  -172.09   0.000    -.1474322   -.1440973
        434  |   -.003759   .0001227   -30.64   0.000    -.0040005   -.0035174
        436  |   .1458566   .0003602   404.94   0.000     .1451475    .1465657
        440  |    .041306   .0020503    20.15   0.000     .0372698    .0453422
        441  |   .1159551   .0001423   814.99   0.000     .1156751    .1162352
        444  |  -.0268415   .0000807  -332.56   0.000    -.0270003   -.0266826
        445  |   .0926828   .0002127   435.68   0.000      .092264    .0931016
        475  |   .1927094   .0000222  8694.12   0.000     .1926658     .192753
        481  |   .0204757   .0000792   258.40   0.000     .0203197    .0206316
        483  |    .045279   .0013299    34.05   0.000     .0426609    .0478971
        484  |  -.0360678     .00302   -11.94   0.000    -.0420131   -.0301226
        489  |   .7139322     .00302   236.40   0.000     .7079869    .7198774
        491  |   .0104757   .0000792   132.20   0.000     .0103197    .0106316
        494  |   .1198402    .000312   384.08   0.000     .1192259    .1204544
        495  |   .7079727   .0020503   345.30   0.000     .7039364    .7120089
        498  |  -.0390476   .0004849   -80.53   0.000    -.0400021   -.0380931
        499  |  -.0995646   .0003602  -276.42   0.000    -.1002737   -.0988556
        500  |   .0853794   2.66e-06  3.2e+04   0.000     .0853741    .0853846
        503  |  -.0826648   .0000478 -1730.46   0.000    -.0827588   -.0825707
        505  |   .0272124   .0008852    30.74   0.000     .0254699    .0289549
        507  |  -.0354104   .0002032  -174.28   0.000    -.0358104   -.0350104
        508  |  -.1466161   .0001227 -1194.90   0.000    -.1468577   -.1463746
        529  |  -.2890476   .0004849  -596.13   0.000    -.2900021   -.2880931
        531  |  -.0785774   .0001936  -405.96   0.000    -.0789584   -.0781963
        535  |   .0971314   .0001653   587.57   0.000      .096806    .0974569
        536  |  -.0818428   .0007471  -109.54   0.000    -.0833136    -.080372
        538  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        541  |  -.1213877   .0013299   -91.27   0.000    -.1240058   -.1187696
        543  |   .0284299   .0001144   248.61   0.000     .0282048     .028655
        545  |   .1109524   .0004849   228.83   0.000     .1099979    .1119069
        560  |  -.2905375   .0007827  -371.19   0.000    -.2920783   -.2889966
        562  |  -.0664943   .0007666   -86.74   0.000    -.0680033   -.0649852
        563  |  -.0225796   .0003159   -71.49   0.000    -.0232014   -.0219578
        564  |   .0746634   .0000443  1685.83   0.000     .0745762    .0747506
        577  |    -.04653   .0000271 -1715.10   0.000    -.0465835   -.0464766
        578  |   .0248124   .0001227   202.22   0.000     .0245709     .025054
        581  |   .0101011   .0002395    42.18   0.000     .0096296    .0105725
        583  |   .1115484   .0009919   112.46   0.000     .1095957     .113501
        584  |   .2105142    .000112  1878.76   0.000     .2102936    .2107348
        588  |   .0672439   .0002395   280.81   0.000     .0667725    .0677153
        592  |    .063134   .0001614   391.28   0.000     .0628164    .0634517
        593  |  -.1319056   .0001556  -847.75   0.000    -.1322119   -.1315993
        595  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        598  |   .2428952   .0006982   347.88   0.000     .2415207    .2442697
        599  |  -.1069585   .0007153  -149.52   0.000    -.1083667   -.1055503
        601  |   .0859524   .0004849   177.27   0.000     .0849979    .0869069
        604  |   .1150049   .0001187   968.51   0.000     .1147711    .1152386
        607  |  -.0108787   .0002816   -38.63   0.000     -.011433   -.0103244
        608  |   .0545162   .0003264   167.01   0.000     .0538736    .0551588
        609  |   .0551098   .0000148  3735.80   0.000     .0550807    .0551388
        611  |   .2139322     .00302    70.84   0.000     .2079869    .2198774
        614  |  -.0770167    .000408  -188.74   0.000    -.0778199   -.0762134
        615  |  -.0029076    .000847    -3.43   0.001    -.0045751   -.0012401
        620  |  -.0508354   .0010362   -49.06   0.000    -.0528754   -.0487955
        623  |  -.0227782   .0001468  -155.12   0.000    -.0230673   -.0224891
        624  |  -.0929107   .0005346  -173.80   0.000    -.0939631   -.0918584
        625  |  -.0390476   .0004849   -80.53   0.000    -.0400021   -.0380931
        626  |  -.0086072   .0002625   -32.79   0.000    -.0091239   -.0080905
        630  |   .2094625   .0007827   267.61   0.000     .2079217    .2110034
        635  |   .0436898   .0000222  1971.07   0.000     .0436462    .0437334
        636  |  -.1483188    .001326  -111.86   0.000    -.1509291   -.1457085
        638  |  -.0166033   .0001233  -134.62   0.000    -.0168461   -.0163605
        678  |   .0432925   .0003602   120.19   0.000     .0425834    .0440016
        680  |   .0036759   .0000808    45.48   0.000     .0035168    .0038351
        681  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        683  |   .1596664   7.23e-06  2.2e+04   0.000     .1596521    .1596806
        684  |   .0527028   .0004568   115.38   0.000     .0518036     .053602
        686  |   .7139322     .00302   236.40   0.000     .7079869    .7198774
        687  |   .0428952   .0006982    61.44   0.000     .0415207    .0442697
        689  |   .2139322     .00302    70.84   0.000     .2079869    .2198774
        691  |   .1071629   .0000383  2800.78   0.000     .1070876    .1072383
        694  |  -.1050764    .000548  -191.75   0.000    -.1061551   -.1039976
        697  |  -.1080421   .0002065  -523.10   0.000    -.1084487   -.1076355
        698  |  -.0128139   .0000694  -184.73   0.000    -.0129504   -.0126773
        700  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        702  |   .7139322     .00302   236.40   0.000     .7079869    .7198774
        704  |  -.1233742   .0003602  -342.53   0.000    -.1240832   -.1226651
        707  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        710  |  -.1884516   .0009919  -189.99   0.000    -.1904043    -.186499
        729  |  -.0320054   .0001586  -201.85   0.000    -.0323175   -.0316932
        732  |   .1139322     .00302    37.73   0.000     .1079869    .1198774
        734  |  -.0044796   .0000727   -61.62   0.000    -.0046227   -.0043365
        738  |   .2079727   .0020503   101.43   0.000     .2039364    .2120089
        739  |   .2079727   .0020503   101.43   0.000     .2039364    .2120089
        740  |   .0693723   .0015714    44.15   0.000     .0662789    .0724657
        742  |  -.2890476   .0004849  -596.13   0.000    -.2900021   -.2880931
        743  |   .1071629   .0000383  2800.78   0.000     .1070876    .1072383
        746  |   .0628487   .0000441  1426.70   0.000      .062762    .0629354
        747  |   .1266258   .0003602   351.55   0.000     .1259168    .1273349
        748  |   .3756326   .0012052   311.66   0.000     .3732599    .3780052
        749  |   .2109524   .0004849   435.07   0.000     .2099979    .2119069
        751  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        753  |    .022726   .0001262   180.03   0.000     .0224775    .0229745
        755  |  -.2903246   .0006016  -482.56   0.000     -.291509   -.2891403
        758  |  -.1358891   .0001002 -1356.57   0.000    -.1360863   -.1356919
        759  |   .0646225   .0000623  1036.61   0.000     .0644997    .0647452
        761  |  -.0189852   .0000375  -506.46   0.000     -.019059   -.0189114
        762  |  -.1054644   .0000489 -2158.87   0.000    -.1055605   -.1053682
        765  |  -.0304621   .0001383  -220.21   0.000    -.0307344   -.0301898
        768  |   .0809917   .0002032   398.61   0.000     .0805917    .0813917
        777  |   .4594625   .0007827   587.01   0.000     .4579217    .4610034
        778  |  -.0375577   .0017525   -21.43   0.000    -.0410076   -.0341078
        781  |   .0322835   .0005783    55.83   0.000     .0311451    .0334219
        783  |  -.0420273   .0020503   -20.50   0.000    -.0460636   -.0379911
        785  |  -.0031914   .0006056    -5.27   0.000    -.0043836   -.0019992
        790  |  -.0160494   .0007153   -22.44   0.000    -.0174576   -.0146412
        791  |   .2094625   .0007827   267.61   0.000     .2079217    .2110034
        831  |  -.0405375   .0007827   -51.79   0.000    -.0420783   -.0389966
        832  |   .2099592   .0003602   582.91   0.000     .2092501    .2106682
        834  |    .045279   .0013299    34.05   0.000     .0426609    .0478971
        837  |   .1209194   .0000156  7769.89   0.000     .1208888      .12095
        845  |   .0995102   .0002032   489.76   0.000     .0991102    .0999102
        846  |  -.2860678     .00302   -94.72   0.000    -.2920131   -.2801226
        848  |  -.0397925   .0001489  -267.20   0.000    -.0400857   -.0394994
        849  |   .2105461   .0001392  1512.94   0.000     .2102721      .21082
        850  |   .1001724   .0007666   130.68   0.000     .0986633    .1016814
        851  |  -.0542793   .0000375 -1448.00   0.000    -.0543531   -.0542055
        853  |   .1464952   .0001077  1360.67   0.000     .1462833    .1467072
        854  |  -.0046103   .0006016    -7.66   0.000    -.0057947    -.003426
        857  |  -.2860678     .00302   -94.72   0.000    -.2920131   -.2801226
        858  |  -.1280934   .0005429  -235.94   0.000    -.1291622   -.1270247
        859  |   .0948802   .0001002   947.18   0.000      .094683    .0950774
        886  |  -.1463323   .0003641  -401.85   0.000    -.1470492   -.1456155
        887  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        889  |  -.0114354    .000344   -33.24   0.000    -.0121126   -.0107581
        890  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        892  |  -.1655375   .0007827  -211.49   0.000    -.1670783   -.1639966
        893  |  -.1203944    .002175   -55.35   0.000    -.1246761   -.1161127
        895  |  -.0356402   .0000777  -458.89   0.000    -.0357931   -.0354873
        905  |   .0713176   .0000438  1629.53   0.000     .0712315    .0714038
        908  |  -.0460012   .0002156  -213.32   0.000    -.0464257   -.0455767
        915  |   .0097605   .0005292    18.44   0.000     .0087187    .0108023
        918  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        922  |   .0432925   .0003602   120.19   0.000     .0425834    .0440016
        924  |   .1115484   .0009919   112.46   0.000     .1095957     .113501
        925  |  -.0937841   .0001542  -608.22   0.000    -.0940876   -.0934805
        927  |  -.0118003   .0001746   -67.59   0.000     -.012144   -.0114566
        931  |  -.1010305   .0001542  -655.21   0.000     -.101334   -.1007269
        934  |  -.1194012     .00302   -39.54   0.000    -.1253464   -.1134559
        935  |   .0960262   .0008749   109.76   0.000     .0943039    .0977485
        936  |  -.2920273   .0020503  -142.43   0.000    -.2960636   -.2879911
        946  |   .1565454   .0001019  1536.90   0.000     .1563449    .1567459
        976  |   .2109524   .0004849   435.07   0.000     .2099979    .2119069
        977  |   .0222209   .0000222  1002.50   0.000     .0221772    .0222645
        980  |  -.0165912   .0002544   -65.22   0.000     -.017092   -.0160904
        981  |  -.0396063   9.52e-06 -4158.48   0.000     -.039625   -.0395875
        989  |  -.1572205   .0000375 -4194.14   0.000    -.1572943   -.1571467
        992  |  -.1948027   .0003602  -540.83   0.000    -.1955118   -.1940937
             |
       _cons |   .2860678     .00302    94.72   0.000     .2801226    .2920131
------------------------------------------------------------------------------

. estadd local Controls "No"

added macro:
           e(Controls) : "No"

. estadd local Controls1 " "

added macro:
          e(Controls1) : " "

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "Yes"

added macro:
          e(Controls5) : "Yes"

. mean voted22 if e(sample)==1 & treatedf_i==0

Mean estimation                         Number of obs = 11,715

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3201878   .0043107      .3117382    .3286374
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M4

. eststo M5:  reg voted22 treatedf_i molincome female age foreign i.moses1d firstvote i.mohighschool i.kunta19, cl
> uster(kunta19)

Linear regression                               Number of obs     =     28,302
                                                F(14, 276)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0815
                                                Root MSE          =     .45109

                                (Std. err. adjusted for 277 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
    treatedf_i |   .0054867    .005111     1.07   0.284    -.0045748    .0155482
     molincome |   .0013035   .0044758     0.29   0.771    -.0075076    .0101146
        female |   .0853413   .0065132    13.10   0.000     .0725194    .0981631
           age |   .0077434   .0009951     7.78   0.000     .0057845    .0097022
       foreign |  -.1594404   .0119276   -13.37   0.000     -.182921   -.1359597
               |
       moses1d |
            1  |   .1844451   .0260615     7.08   0.000     .1331405    .2357496
            2  |   .0661626   .0194731     3.40   0.001     .0278278    .1044973
            3  |   .1359338   .0154269     8.81   0.000     .1055644    .1663032
            4  |   .0524791   .0133929     3.92   0.000     .0261139    .0788443
            5  |   .0122451   .0132386     0.92   0.356    -.0138163    .0383065
            6  |   .0788522   .0153081     5.15   0.000     .0487167    .1089878
            7  |   .0895772   .0191253     4.68   0.000     .0519271    .1272273
            9  |   .0350149   .0280985     1.25   0.214    -.0202996    .0903295
               |
     firstvote |   .1165461   .0124728     9.34   0.000     .0919922       .1411
1.mohighschool |   .1350965   .0060968    22.16   0.000     .1230944    .1470985
               |
       kunta19 |
            9  |    .271584   .0021545   126.06   0.000     .2673427    .2758252
           10  |  -.0000514   .0019934    -0.03   0.979    -.0039756    .0038729
           18  |  -.2482069   .0051939   -47.79   0.000    -.2584316   -.2379822
           19  |   .0143876   .0027102     5.31   0.000     .0090523    .0197229
           20  |  -.1404133   .0048122   -29.18   0.000    -.1498865   -.1309401
           43  |  -.1517018   .0191035    -7.94   0.000    -.1893088   -.1140947
           47  |  -.2987597   .0046213   -64.65   0.000    -.3078572   -.2896622
           49  |   .0096434   .0036225     2.66   0.008     .0025123    .0167746
           50  |   .2418729   .0048651    49.72   0.000     .2322955    .2514504
           51  |   .0011225    .000911     1.23   0.219     -.000671     .002916
           52  |   .8091648   .0101796    79.49   0.000     .7891253    .8292043
           60  |  -.2377976    .008119   -29.29   0.000    -.2537805   -.2218146
           61  |   .0344205   .0051694     6.66   0.000     .0242441    .0445969
           69  |   .0577464   .0014492    39.85   0.000     .0548935    .0605994
           71  |   .0711761   .0014528    48.99   0.000     .0683162     .074036
           72  |   .0531115   .0033237    15.98   0.000     .0465684    .0596545
           74  |   .0783048   .0022735    34.44   0.000     .0738291    .0827804
           75  |  -.3391317   .0038544   -87.99   0.000    -.3467194    -.331544
           77  |  -.0549721   .0013767   -39.93   0.000    -.0576822    -.052262
           78  |  -.2299803   .0047686   -48.23   0.000    -.2393677   -.2205929
           79  |  -.1177883   .0018725   -62.90   0.000    -.1214745    -.114102
           81  |  -.2757592   .0087284   -31.59   0.000    -.2929419   -.2585766
           82  |  -.2550371   .0056967   -44.77   0.000    -.2662516   -.2438227
           86  |   -.040464   .0012947   -31.25   0.000    -.0430127   -.0379153
           90  |  -.2947826   .0049846   -59.14   0.000    -.3045953     -.28497
           91  |   .0633526    .003461    18.30   0.000     .0565393    .0701658
           92  |   -.016496   .0020968    -7.87   0.000    -.0206237   -.0123683
           98  |  -.0767599   .0062925   -12.20   0.000    -.0891474   -.0643724
           99  |  -.0058947   .0085815    -0.69   0.493    -.0227883    .0109988
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          934  |  -.1036205   .0042665   -24.29   0.000    -.1120195   -.0952214
          935  |   .0934243   .0012633    73.95   0.000     .0909374    .0959111
          936  |  -.1740025   .0071759   -24.25   0.000    -.1881288   -.1598761
          946  |   .1518065   .0015019   101.08   0.000     .1488499    .1547632
          976  |   .1299218   .0068251    19.04   0.000     .1164859    .1433578
          977  |   .0304618   .0021637    14.08   0.000     .0262024    .0347211
          980  |   .0020983   .0028877     0.73   0.468    -.0035864     .007783
          981  |  -.0261195   .0029526    -8.85   0.000    -.0319319   -.0203071
          989  |   -.144359   .0011952  -120.78   0.000    -.1467118   -.1420062
          992  |  -.2068577   .0033942   -60.94   0.000    -.2135395   -.2001759
               |
         _cons |  -.0334996   .0432795    -0.77   0.440    -.1186995    .0517002
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "Yes"

added macro:
          e(Controls5) : "Yes"

. mean voted22 if e(sample)==1 & treatedf_i==0

Mean estimation                         Number of obs = 11,604

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |     .32101   .0043342      .3125143    .3295057
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M5

. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local numbers "& (1) & (2) & (3) & (4) & (5) \\ \hline"

. local emptyrow "& & & & &  \\ "

. local line "& & & & & \hline \\ "

. 
. local dstars " "

. local sestars " "

. esttab, keep (treatedf_i) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("Controls Controls" "Controls1 \phantom{controls}"  "Controls2 \phantom{controls}" "Controls3 \phantom{c
> ontrols}" "Controls4 \phantom{controls}"  "Controls5 Municipality FE" "umean Untreated $\bar{Y}$" "N Observation
> s") ///
> sfmt(%9.3f %9.3f %9.3f %9.3f  %9.3f  %9.3f  %9.3f %9.0fc) mlabels(none) nonumbers posthead("`header'" "`emptyrow
> '" "`numbers'" "`emptyrow'") ///
> refcat(treatedf_i "", nolabel below) title("Average Treatment Effect") nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

Average Treatment Effect
----------------------------------------------------------------------------------------------------
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & & &  \\ 
& (1) & (2) & (3) & (4) & (5) \\ \hline
& & & & &  \\ 
Treatments Pooled           0.006           0.006           0.006           0.006           0.005   
                          (0.005)         (0.005)         (0.005)         (0.005)         (0.005)   

                                                                                                    
----------------------------------------------------------------------------------------------------
Controls                       No    Female, Age, Immigrant,    Female, Age, Immigrant,              No    Female,
>  Age, Immigrant,   
\phantom{controls}                      Ln Income      Ln Income,                      Ln Income,   
\phantom{controls}                                   SES Background,                    SES Background,   
\phantom{controls}                                   Educational Background,                    Educational Backgr
> ound,   
\phantom{controls}                                   First Time Eligble to Vote                    First Time Elig
> ble to Vote   
Municipality FE                No              No              No             Yes             Yes   
Untreated $\bar{Y}$         0.320           0.321           0.321           0.320           0.321   
Observations               28,564          28,302          28,302          28,564          28,302   
----------------------------------------------------------------------------------------------------

. 
. *************************************************
. *****TABLE 6
. *************************************************
. 
. 
. 
. eststo clear

. 
. 
. eststo old: reg voted22 treatedf molincome age i.moses1d firstvote i.mohighschool female foreign if old==1 & not
> samehh!=1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     13,054
                                                F(14, 166)        =     140.13
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0909
                                                Root MSE          =     .46054

                                (Std. err. adjusted for 167 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0180338     .01117     1.61   0.108    -.0040198    .0400874
     molincome |   .0452089   .0071971     6.28   0.000     .0309991    .0594186
           age |   .0083953   .0012369     6.79   0.000     .0059532    .0108373
               |
       moses1d |
            1  |   .2319896    .025733     9.02   0.000     .1811833    .2827958
            2  |   .0795069   .0255613     3.11   0.002     .0290397    .1299742
            3  |   .1680561   .0209405     8.03   0.000     .1267121    .2094002
            4  |   .0856569   .0207101     4.14   0.000     .0447678     .126546
            5  |  -.0259447   .0179774    -1.44   0.151    -.0614385    .0095492
            6  |   .1018765   .0485213     2.10   0.037      .006078     .197675
            7  |   .0118029   .0240021     0.49   0.624    -.0355859    .0591917
            9  |    .054921   .0456628     1.20   0.231    -.0352336    .1450757
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1396216   .0104553    13.35   0.000     .1189791    .1602641
        female |  -.0152854   .0072754    -2.10   0.037    -.0296496   -.0009212
       foreign |  -.3041566   .0274295   -11.09   0.000    -.3583122    -.250001
         _cons |  -.4325604   .1146484    -3.77   0.000    -.6589173   -.2062036
--------------------------------------------------------------------------------

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store old

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: old

. 
. 
. 
. 
. eststo young: reg voted22 treatedf molincome age i.moses1d firstvote i.mohighschool female foreign if old==0 & n
> otsamehh!=1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     23,822
                                                F(14, 256)        =     422.87
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1219
                                                Root MSE          =     .46472

                                (Std. err. adjusted for 257 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0099437   .0062113     1.60   0.111     -.002288    .0221754
     molincome |   .0127413   .0044975     2.83   0.005     .0038845    .0215981
           age |   .0096777   .0004359    22.20   0.000     .0088194    .0105361
               |
       moses1d |
            1  |   .2167893   .0288821     7.51   0.000     .1599126     .273666
            2  |   .1048408   .0179507     5.84   0.000      .069491    .1401906
            3  |   .1787259   .0139759    12.79   0.000     .1512035    .2062482
            4  |    .088313   .0136956     6.45   0.000     .0613426    .1152833
            5  |   .0162084   .0138741     1.17   0.244    -.0111135    .0435303
            6  |   .1258599   .0164695     7.64   0.000     .0934269    .1582929
            7  |   .0319062   .0212451     1.50   0.134    -.0099311    .0737435
            9  |   .0623378    .026669     2.34   0.020     .0098193    .1148564
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1498481   .0087852    17.06   0.000     .1325477    .1671484
        female |   .0206712   .0049443     4.18   0.000     .0109346    .0304078
       foreign |  -.2313388   .0106296   -21.76   0.000    -.2522714   -.2104062
         _cons |  -.1627293    .055512    -2.93   0.004    -.2720476    -.053411
--------------------------------------------------------------------------------

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. mystars diffe stde df

. local dstars1=e(d_stars)

. local sestars1=e(se_stars)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store young

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: young

. 
. 
. eststo notsamehhold: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if not
> samehh==1 & old==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     16,050
                                                F(14, 217)        =     152.05
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0894
                                                Root MSE          =     .46388

                                (Std. err. adjusted for 218 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |  -.0051559   .0079146    -0.65   0.515    -.0207552    .0104434
     molincome |   .0736937   .0082725     8.91   0.000      .057389    .0899985
        female |     .00488   .0057665     0.85   0.398    -.0064856    .0162456
           age |   .0137474   .0009161    15.01   0.000     .0119417    .0155531
               |
       moses1d |
            1  |   .2510459   .0276409     9.08   0.000     .1965669    .3055249
            2  |   .1169255   .0185104     6.32   0.000     .0804423    .1534086
            3  |   .1511471     .02027     7.46   0.000     .1111958    .1910983
            4  |   .0720384   .0190218     3.79   0.000     .0345472    .1095295
            5  |  -.0182511   .0200315    -0.91   0.363    -.0577324    .0212302
            6  |   .1226193    .048116     2.55   0.012     .0277849    .2174538
            7  |   .0078935   .0220506     0.36   0.721    -.0355672    .0513542
            9  |  -.0062018   .0398865    -0.16   0.877    -.0848165    .0724128
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1316776   .0091903    14.33   0.000     .1135639    .1497913
       foreign |  -.2645258   .0174182   -15.19   0.000    -.2988563   -.2301954
         _cons |  -1.027138   .0847513   -12.12   0.000    -1.194179   -.8600966
--------------------------------------------------------------------------------

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store notsamehhold

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: notsamehhold

. 
. 
. 
. 
. eststo notsamehhyoung: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign  if 
> notsamehh==1 & old==0, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     21,577
                                                F(14, 270)        =     298.84
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0844
                                                Root MSE          =     .45871

                                (Std. err. adjusted for 271 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0081671   .0068722     1.19   0.236    -.0053629    .0216971
     molincome |   .0274688   .0053777     5.11   0.000     .0168812    .0380564
        female |   .0676178   .0060284    11.22   0.000     .0557492    .0794865
           age |   .0079803   .0003545    22.51   0.000     .0072823    .0086782
               |
       moses1d |
            1  |   .1766316   .0291622     6.06   0.000     .1192175    .2340458
            2  |    .066787   .0148515     4.50   0.000     .0375476    .0960264
            3  |   .1335152   .0152487     8.76   0.000     .1034938    .1635367
            4  |    .048658   .0117556     4.14   0.000     .0255137    .0718023
            5  |  -.0124835   .0105595    -1.18   0.238    -.0332729     .008306
            6  |   .0568284   .0225095     2.52   0.012      .012512    .1011448
            7  |   .0271509   .0200464     1.35   0.177    -.0123162     .066618
            9  |   .0101414   .0220177     0.46   0.645    -.0332068    .0534895
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1122714   .0119042     9.43   0.000     .0888345    .1357083
       foreign |  -.2023417   .0307982    -6.57   0.000    -.2629768   -.1417066
         _cons |  -.2832561   .0593659    -4.77   0.000     -.400135   -.1663772
--------------------------------------------------------------------------------

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. mystars diffe stde df

. local dstars2=e(d_stars)

. local sestars2=e(se_stars)

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store notsamehhyoung

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: notsamehhyoung

. 
. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 "& \multicolumn{2}{c}{{Currently living in the HH} & \multicolumn{2}{c}{Currently not living in th
> e HH} \\ "

. local titles2 "& Over 49 & Under 50 & Over 49 & Under 50\\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "dstars \hline Differences  & \multico
> lumn{2}{c}{`dstars1'} & \multicolumn{2}{c}{`dstars2'} \\ %" "sestars \phantom{sestars} & \multicolumn{2}{c}{`ses
> tars1'} & \multicolumn{2}{c}{`sestars2'} \\ %") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`header'" "`emptyrow'" `"`titl
> es1'"' "`titles2'" "`numbers'"  "`emptyrow'")   ///
> refcat(treatedf "", nolabel below) postfoot(\hline\hline \\ \end{tabular} \\ \end{table}) fragment  nonotes repl
> ace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& \multicolumn{4}{c}{Outcome: Voted} \\
& & & &  \\ 
& \multicolumn{2}{c}{{Currently living in the HH} & \multicolumn{2}{c}{Currently not living in the HH} \\ 
& Over 49 & Under 50 & Over 49 & Under 50\\
& (1) & (2) & (3) & (4) \\ \hline
& & & &  \\ 
Spillover Treatment         0.018           0.010          -0.005           0.008   
                          (0.011)         (0.006)         (0.008)         (0.007)   

                                                                                    
\hline
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.620           0.428           0.624           0.353   
Observations               13,054          23,822          16,050          21,577   
\hline Differ..0~013                                                                
\phantom{sest..01~01                                                                
\hline\hline \\ \end{tabular} \\ \end{table}

. ******************************************************************************************
. 
. 
. 
. *************************************************
. *****TABLE A12
. *************************************************
. 
. 
. 
. eststo clear

. eststo M1: reg voted22 treatedfa, cluster(kunta19)

Linear regression                               Number of obs     =     39,943
                                                F(1, 260)         =      11.35
                                                Prob > F          =     0.0009
                                                R-squared         =     0.0005
                                                Root MSE          =     .49978

                              (Std. err. adjusted for 261 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatedfa |   .0218522    .006487     3.37   0.001     .0090784     .034626
       _cons |   .4975757   .0075503    65.90   0.000     .4827081    .5124433
------------------------------------------------------------------------------

. estadd local Controls "No"

added macro:
           e(Controls) : "No"

. estadd local Controls1 " "

added macro:
          e(Controls1) : " "

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedfa==0

Mean estimation                         Number of obs = 15,262

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .4975757   .0040474      .4896423     .505509
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M1

. eststo M2: reg voted22 treatedfa molincome female age foreign, cluster(kunta19)

Linear regression                               Number of obs     =     39,591
                                                F(5, 260)         =     364.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0947
                                                Root MSE          =     .47562

                              (Std. err. adjusted for 261 clusters in kunta19)
------------------------------------------------------------------------------
             |               Robust
     voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatedfa |   .0180562   .0055044     3.28   0.001     .0072174    .0288949
   molincome |   .0682206   .0047163    14.46   0.000     .0589335    .0775076
      female |   .0503367   .0038801    12.97   0.000     .0426963     .057977
         age |   .0082479   .0002804    29.42   0.000     .0076959       .0088
     foreign |  -.3063349   .0131854   -23.23   0.000    -.3322988   -.2803711
       _cons |  -.5552722   .0603266    -9.20   0.000    -.6740632   -.4364812
------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income"

added macro:
          e(Controls1) : "Ln Income"

. estadd local Controls2 " "

added macro:
          e(Controls2) : " "

. estadd local Controls3 " "

added macro:
          e(Controls3) : " "

. estadd local Controls4 " "

added macro:
          e(Controls4) : " "

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedfa==0

Mean estimation                         Number of obs = 15,112

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .4991398   .0040675      .4911671    .5071125
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M2

. eststo M3:  reg voted22 treatedfa molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(kunta
> 19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     39,591
                                                F(14, 260)        =     708.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1400
                                                Root MSE          =     .46362

                                (Std. err. adjusted for 261 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
     treatedfa |   .0172992   .0056627     3.05   0.002     .0061485    .0284498
     molincome |   .0260659   .0040839     6.38   0.000     .0180241    .0341076
        female |   .0104618   .0045223     2.31   0.021     .0015568    .0193667
           age |   .0086937   .0002512    34.61   0.000     .0081991    .0091882
       foreign |  -.2516252   .0115378   -21.81   0.000    -.2743445   -.2289059
               |
       moses1d |
            1  |   .2173106   .0180812    12.02   0.000     .1817064    .2529149
            2  |    .096513   .0174168     5.54   0.000      .062217    .1308091
            3  |   .1709361   .0116294    14.70   0.000     .1480362     .193836
            4  |   .0807024   .0115969     6.96   0.000     .0578665    .1035382
            5  |   -.001663   .0101538    -0.16   0.870    -.0216573    .0183312
            6  |    .125026   .0185422     6.74   0.000     .0885139     .161538
            7  |   .0074647   .0163892     0.46   0.649    -.0248079    .0397372
            9  |   .0515681   .0249661     2.07   0.040     .0024065    .1007297
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1431021   .0082257    17.40   0.000     .1269047    .1592996
         _cons |  -.2555092   .0483536    -5.28   0.000    -.3507238   -.1602947
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "No"

added macro:
          e(Controls5) : "No"

. mean voted22 if e(sample)==1 & treatedfa==0

Mean estimation                         Number of obs = 15,112

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .4991398   .0040675      .4911671    .5071125
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M3

. eststo M4:  reg voted22 treatedfa molincome female age foreign i.moses1d firstvote i.mohighschool i.totpottreat,
>  cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     39,591
                                                F(16, 260)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1408
                                                Root MSE          =     .46344

                                (Std. err. adjusted for 261 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
     treatedfa |   .0141551   .0057449     2.46   0.014     .0028426    .0254676
     molincome |   .0262457   .0040714     6.45   0.000     .0182286    .0342628
        female |   .0104624   .0044708     2.34   0.020     .0016587     .019266
           age |   .0085689   .0002525    33.93   0.000     .0080716    .0090661
       foreign |  -.2522417   .0121262   -20.80   0.000    -.2761198   -.2283636
               |
       moses1d |
            1  |   .2141555   .0180504    11.86   0.000     .1786118    .2496991
            2  |    .096071   .0173395     5.54   0.000     .0619273    .1302147
            3  |   .1705564   .0116253    14.67   0.000     .1476646    .1934482
            4  |   .0802021   .0116721     6.87   0.000     .0572182     .103186
            5  |  -.0014855   .0101075    -0.15   0.883    -.0213885    .0184176
            6  |   .1252876   .0185233     6.76   0.000     .0888129    .1617623
            7  |    .009606   .0164397     0.58   0.560    -.0227659    .0419778
            9  |   .0516618   .0252732     2.04   0.042     .0018956     .101428
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1435036    .008143    17.62   0.000      .127469    .1595383
               |
   totpottreat |
            2  |   .0512408   .0114327     4.48   0.000     .0287283    .0737532
            3  |   .0381244   .0544537     0.70   0.484     -.069102    .1453508
            4  |   .3075055   .0174891    17.58   0.000     .2730672    .3419438
            5  |   .2837406   .0190813    14.87   0.000      .246167    .3213142
            6  |   .6890097   .0167786    41.06   0.000     .6559704     .722049
               |
         _cons |  -.2537421   .0482892    -5.25   0.000    -.3488297   -.1586545
--------------------------------------------------------------------------------

. estadd local Controls "Female, Age, Immigrant,"

added macro:
           e(Controls) : "Female, Age, Immigrant,"

. estadd local Controls1 "Ln Income,"

added macro:
          e(Controls1) : "Ln Income,"

. estadd local Controls2 "SES Background,"

added macro:
          e(Controls2) : "SES Background,"

. estadd local Controls3 "Educational Background,"

added macro:
          e(Controls3) : "Educational Background,"

. estadd local Controls4 "First Time Eligble to Vote"

added macro:
          e(Controls4) : "First Time Eligble to Vote"

. estadd local Controls5 "Yes"

added macro:
          e(Controls5) : "Yes"

. mean voted22 if e(sample)==1 & treatedfa==0

Mean estimation                         Number of obs = 15,112

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .4991398   .0040675      .4911671    .5071125
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M4

. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. local dstars " "

. local sestars " "

. esttab, keep (treatedfa) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("Controls Controls" "Controls1 \phantom{controls}"  "Controls2 \phantom{controls}" "Controls3 \phantom{c
> ontrols}" "Controls4 \phantom{controls}"  "Controls5 N Potentially Treated in HH" "umean Untreated $\bar{Y}$" "N
>  Observations") ///
> sfmt(%9.3f %9.3f %9.3f %9.3f  %9.3f  %9.3f  %9.3f %9.0fc) mlabels(none) nonumbers posthead("`header'" "`emptyrow
> '" "`numbers'" "`emptyrow'") ///
> refcat(treatedf_i "", nolabel below) title("Average Treatment Effect") nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

Average Treatment Effect
------------------------------------------------------------------------------------
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & &  \\ 
& (1) & (2) & (3) & (4) \\ \hline
& & & &  \\ 
treatedfa                   0.022***        0.018***        0.017***        0.014** 
                          (0.006)         (0.006)         (0.006)         (0.006)   
------------------------------------------------------------------------------------
Controls                       No    Female, Age, Immigrant,    Female, Age, Immigrant,    Female, Age, Immigrant,
>    
\phantom{controls}                      Ln Income      Ln Income,      Ln Income,   
\phantom{controls}                                   SES Background,    SES Background,   
\phantom{controls}                                   Educational Background,    Educational Background,   
\phantom{controls}                                   First Time Eligble to Vote    First Time Eligble to Vote   
N Potentially Trea~H           No              No              No             Yes   
Untreated $\bar{Y}$         0.498           0.499           0.499           0.499   
Observations               39,943          39,591          39,591          39,591   
------------------------------------------------------------------------------------

. 
. 
. 
. 
. 
end of do-file

. do "$home\dofiles\TablesA5-A7.do"

. ****************************************************************************************************************
> ****
. *****Do-file for Tables A5-7
. *****Who is mobilized to vote by short text messages? Evidence from a nationwide field experiment with young vot
> ers
. ****************************************************************************************************************
> ****
. *****Last edited 24/6/5
. ****************************************************************************************************************
> ****
. *****Ado packages needed: estout
. ****************************************************************************************************************
> ****
. 
. 
. clear all

. 
. 
. use \data\dataforanalysis230522_v2.dta, clear

. 
. 
. replace age=age+3 //replace age for age in 2022
(3,562,582 real changes made)

. 
. rename svatva_k income

. keep if treated!=.
(3,523,183 observations deleted)

. 
. *************************************************
. *****TABLE A5
. *************************************************
. 
. eststo s1: estpost tabstat female age highschool income foreign if mhighschool!=.,statistics(mean sd) columns(st
> atistics)

Summary statistics: mean sd
     for variables: female age highschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .3929094   .4884043 
         age |  23.15127   2.567532 
  highschool |  .3268077   .4690533 
      income |  12410.49   11599.83 
     foreign |  .0270375   .1621952 

. eststo s2: estpost tabstat female age highschool income foreign if mhighschool==.,statistics(mean sd) columns(st
> atistics)

Summary statistics: mean sd
     for variables: female age highschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .4259891   .4945061 
         age |  27.47452   2.000506 
  highschool |   .389211   .4875852 
      income |  21910.64   13618.41 
     foreign |  .0672682   .2504932 

. 
. 
. esttab, cells(mean(fmt(2)) sd(fmt(2) par)) unstack nonote nonumber noobs stats(N, fmt(a) labels("Observations"))
>  mtitles("Mother Highschool Known" "Mother Highschool Not Known") replace

--------------------------------------
             Mother Hig~n Mother Hig~n
                  mean/sd      mean/sd
--------------------------------------
female               0.39         0.43
                   (0.49)       (0.49)
age                 23.15        27.47
                   (2.57)       (2.00)
highschool           0.33         0.39
                   (0.47)       (0.49)
income           12410.49     21910.64
               (11599.83)   (13618.41)
foreign              0.03         0.07
                   (0.16)       (0.25)
--------------------------------------
Observations        33509        17592
--------------------------------------

. 
. eststo clear

. 
. *************************************************
. *****TABLE A6
. *************************************************
. 
. 
. sum pvote_mother if voted22!=.  & treated!=. & female!=., detail

                         Pr(voted22)
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0714104        .023837
 5%      .118203       .0244854
10%     .1504558       .0245744       Obs              49,458
25%     .2063651       .0256294       Sum of wgt.      49,458

50%     .2950955                      Mean           .3090878
                        Largest       Std. dev.      .1320126
75%     .4018736       .8301099
90%     .4902918       .8364667       Variance       .0174273
95%     .5421607       .8550987       Skewness       .4419521
99%     .6177514       .8821674       Kurtosis       2.669571

. gen marginal=.
(51,101 missing values generated)

. replace marginal=1 if pvote_mother>=r(p25) & pvote_mother<r(p75)
(25,046 real changes made)

. replace marginal=0 if pvote_mother<r(p25) | (pvote_mother>=r(p75) & pvote_mother!=.)
(24,963 real changes made)

. 
. gen never=.
(51,101 missing values generated)

. replace never=1 if pvote_mother<r(p25)
(12,476 real changes made)

. replace never=0 if pvote_mother>=r(p25) & pvote_mother!=.
(37,533 real changes made)

. 
. gen always=.
(51,101 missing values generated)

. replace always=1 if pvote_mother>=r(p75) & pvote_mother!=.
(12,487 real changes made)

. replace always=0 if pvote_mother<r(p75)
(37,522 real changes made)

. 
. eststo clear

. 
. eststo s1: estpost tabstat female age mohighschool income foreign if never==1,statistics(mean sd) columns(statis
> tics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .1428342   .3499178 
         age |  24.92225   3.032999 
mohighschool |  .0453671   .2081163 
      income |  18837.28   14680.37 
     foreign |  .1461206   .3532412 

. eststo s2: estpost tabstat female age mohighschool income foreign if marginal==1,statistics(mean sd) columns(sta
> tistics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .4020602   .4903238 
         age |  24.53697   3.103867 
mohighschool |  .4072107   .4913245 
      income |  15423.02   12725.48 
     foreign |   .006548   .0806557 

. eststo s3: estpost tabstat female age mohighschool income foreign if always==1,statistics(mean sd) columns(stati
> stics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .6854329   .4643618 
         age |  24.47193   3.300575 
mohighschool |  .9103067   .2857533 
      income |  13387.01   11706.14 
     foreign |  .0002402   .0154988 

. 
. 
. esttab, cells(mean(fmt(2)) sd(fmt(2) par)) unstack nonote nonumber noobs stats(N, fmt(a) labels("Observations"))
>  mtitles("Low Propensity" "Marginal Voters" "High Propensity") replace

---------------------------------------------------
             Low Propen~y Marginal V~s High Prope~y
                  mean/sd      mean/sd      mean/sd
---------------------------------------------------
female               0.14         0.40         0.69
                   (0.35)       (0.49)       (0.46)
age                 24.92        24.54        24.47
                   (3.03)       (3.10)       (3.30)
mohighschool         0.05         0.41         0.91
                   (0.21)       (0.49)       (0.29)
income           18837.28     15423.02     13387.01
               (14680.37)   (12725.48)   (11706.14)
foreign              0.15         0.01         0.00
                   (0.35)       (0.08)       (0.02)
---------------------------------------------------
Observations        12476        25046        12487
---------------------------------------------------

. 
. eststo clear

. 
. *************************************************
. *****TABLE A7
. *************************************************
. 
. sum pvote_enet_mother if voted22!=. & treated!=. & female!=., detail

                   Pr(voted22), penalized
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0995974       .0484223
 5%     .1560467       .0484223
10%     .1748806       .0581356       Obs              50,140
25%     .2101384       .0655169       Sum of wgt.      50,140

50%     .2915984                      Mean           .3078214
                        Largest       Std. dev.      .1102293
75%     .3891061       .7372629
90%     .4602669       .7456779       Variance       .0121505
95%     .5081187       .7522143       Skewness       .3950703
99%     .5738017       .7706261       Kurtosis       2.466242

. replace marginal=.
(50,009 real changes made, 50,009 to missing)

. replace marginal=1 if pvote_enet_mother>=r(p25) & pvote_enet_mother<r(p75)
(25,357 real changes made)

. replace marginal=0 if pvote_enet_mother<r(p25) | (pvote_enet_mother>=r(p75) & pvote_enet_mother!=.)
(25,441 real changes made)

. 
. replace never=.
(50,009 real changes made, 50,009 to missing)

. replace never=1 if pvote_enet_mother<r(p25)
(12,768 real changes made)

. replace never=0 if pvote_enet_mother>=r(p25) & pvote_enet_mother!=.
(38,030 real changes made)

. 
. replace always=.
(50,009 real changes made, 50,009 to missing)

. replace always=1 if pvote_enet_mother>=r(p75) & pvote_enet_mother!=.
(12,673 real changes made)

. replace always=0 if pvote_enet_mother<r(p75)
(38,125 real changes made)

. 
. 
. eststo s1: estpost tabstat female age mohighschool income foreign if never==1,statistics(mean sd) columns(statis
> tics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .0646147   .2458543 
         age |  24.88972   3.027364 
mohighschool |  .0101034   .1000104 
      income |  19306.74   14947.32 
     foreign |  .1466949   .3538153 

. eststo s2: estpost tabstat female age mohighschool income foreign if marginal==1,statistics(mean sd) columns(sta
> tistics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .4200812   .4935814 
         age |  24.50992   3.120956 
mohighschool |  .3948811   .4888348 
      income |  15185.56    12577.5 
     foreign |  .0076113   .0869119 

. eststo s3: estpost tabstat female age mohighschool income foreign if always==1,statistics(mean sd) columns(stati
> stics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .7246114   .4467275 
         age |  24.57926   3.299983 
mohighschool |  .9672532   .1779801 
      income |  13388.72   11550.46 
     foreign |  .0002367   .0153846 

. 
. 
. esttab, cells(mean(fmt(2)) sd(fmt(2) par)) unstack nonote nonumber noobs stats(N, fmt(a) labels("Observations"))
>  mtitles("Low Propensity" "Marginal Voters" "High Propensity") replace

---------------------------------------------------
             Low Propen~y Marginal V~s High Prope~y
                  mean/sd      mean/sd      mean/sd
---------------------------------------------------
female               0.06         0.42         0.72
                   (0.25)       (0.49)       (0.45)
age                 24.89        24.51        24.58
                   (3.03)       (3.12)       (3.30)
mohighschool         0.01         0.39         0.97
                   (0.10)       (0.49)       (0.18)
income           19306.74     15185.56     13388.72
               (14947.32)   (12577.50)   (11550.46)
foreign              0.15         0.01         0.00
                   (0.35)       (0.09)       (0.02)
---------------------------------------------------
Observations        12768        25357        12673
---------------------------------------------------

. 
. 
. 
. 
end of do-file

. do "$home\dofiles\TablesA8-9_and_A15-16.do"

. ****************************************************************************************************************
> ****
. *****Do-file for Tables A8-9 and A15-16
. *****Who is mobilized to vote by short text messages? Evidence from a nationwide field experiment with young vot
> ers
. ****************************************************************************************************************
> ****
. *****Last edited 24/6/5
. ****************************************************************************************************************
> ****
. *****Ado packages needed: estout
. ****************************************************************************************************************
> ****
. 
. clear all

. 
. *Programs to calculate group differences with standard errors and stars
. capture program drop, myrepost

. program myrepost, eclass
  1. ereturn repost b=`1'
  2. ereturn repost V=`2'
  3. ereturn scalar df_r=`3'
  4. end

. 
. capture program drop mystars

. program mystars, eclass
  1. local d_stars=string(`1', "%9.3f")
  2. local pval=ttail(`3',abs(`1'/`2'))*2
  3. 
. if `pval'<=0.01 {
  4. local d_stars="`d_stars'"+"***"         
  5. }
  6. 
. if `pval'>0.01 & `pval'<=0.05  {
  7. local d_stars="`d_stars'"+"**"          
  8. }
  9. 
. if `pval'>0.05 & `pval'<=0.1  {
 10. local d_stars="`d_stars'"+"*"           
 11. }
 12. 
. local se_stars=string(`2', "%9.3f")
 13. 
. local se_stars="("+"`se_stars'"+")"
 14. 
. ereturn local d_stars="`d_stars'"
 15. ereturn local se_stars="`se_stars'"
 16. 
. 
. end

. 
. *Use data
. use \data\dataforanalysis230522_v2.dta, clear

. *Set basecategory
. fvset base 8 moses1d

. 
. 
. label variable treatedf "Treated in HH"

. 
. 
. 
. *Generate voting propensity groups for direct effects
. sum pvote_mother if voted22!=.  & treated!=. & female!=., detail

                         Pr(voted22)
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0714104        .023837
 5%      .118203       .0244854
10%     .1504558       .0245744       Obs              49,458
25%     .2063651       .0256294       Sum of wgt.      49,458

50%     .2950955                      Mean           .3090878
                        Largest       Std. dev.      .1320126
75%     .4018736       .8301099
90%     .4902918       .8364667       Variance       .0174273
95%     .5421607       .8550987       Skewness       .4419521
99%     .6177514       .8821674       Kurtosis       2.669571

. gen marginal=.
(3,574,284 missing values generated)

. replace marginal=1 if pvote_mother>=r(p25) & pvote_mother<r(p75)
(1,616,830 real changes made)

. replace marginal=0 if pvote_mother<r(p25) | (pvote_mother>=r(p75) & pvote_mother!=.)
(1,665,387 real changes made)

. 
. gen never=.
(3,574,284 missing values generated)

. replace never=1 if pvote_mother<r(p25)
(857,917 real changes made)

. replace never=0 if pvote_mother>=r(p25) & pvote_mother!=.
(2,424,300 real changes made)

. 
. gen always=.
(3,574,284 missing values generated)

. replace always=1 if pvote_mother>=r(p75) & pvote_mother!=.
(807,470 real changes made)

. replace always=0 if pvote_mother<r(p75)
(2,474,747 real changes made)

. 
. *Generate voting propensity groups for spillover effects
. sum pvoteold if voted22!=.  & treatedf!=. & female!=., detail

                         Pr(voted22)
-------------------------------------------------------------
      Percentiles      Smallest
 1%      .088845        .025952
 5%     .1792009       .0305654
10%     .2285496       .0320571       Obs              36,723
25%     .3446258       .0320964       Sum of wgt.      36,723

50%     .5015987                      Mean            .498383
                        Largest       Std. dev.      .1964743
75%     .6549283       .9408319
90%     .7655475       .9433452       Variance       .0386021
95%     .8062306       .9439933       Skewness      -.0821849
99%     .8595173       .9450071       Kurtosis       2.121713

. gen marginalf=.
(3,574,284 missing values generated)

. replace marginalf=1 if pvoteold>=r(p25) & pvoteold<r(p75)
(1,413,465 real changes made)

. replace marginalf=0 if pvoteold<r(p25) | (pvoteold>=r(p75) & pvoteold!=.)
(1,805,722 real changes made)

. 
. gen neverf=.
(3,574,284 missing values generated)

. replace neverf=1 if pvoteold<r(p25)
(836,853 real changes made)

. replace neverf=0 if pvoteold>=r(p25) & pvoteold!=.
(2,382,334 real changes made)

. 
. gen alwaysf=.
(3,574,284 missing values generated)

. replace alwaysf=1 if pvoteold>=r(p75) & pvoteold!=.
(968,869 real changes made)

. replace alwaysf=0 if pvoteold<r(p75)
(2,250,318 real changes made)

. 
. 
. 
. *Generate dummies for individual and family member vote propensity groups
. gen marginalfitemp=marginal if ntreatedf==1 & treated==1
(3,557,449 missing values generated)

. replace marginalfitemp=marginal if ncontrolf==1 & treated==0
(11,705 real changes made)

. bys petu20: egen marginalfi=mean(marginalfitemp)
(3,491,591 missing values generated)

. 
. gen neverfitemp=never if ntreatedf==1 & treated==1
(3,557,449 missing values generated)

. replace neverfitemp=never if ncontrolf==1 & treated==0
(11,705 real changes made)

. bys petu20: egen neverfi=mean(neverfitemp)
(3,491,591 missing values generated)

. 
. 
. gen alwaysfitemp=always if ntreatedf==1 & treated==1
(3,557,449 missing values generated)

. replace alwaysfitemp=always if ncontrolf==1 & treated==0
(11,705 real changes made)

. bys petu20: egen alwaysfi=mean(alwaysfitemp)
(3,491,591 missing values generated)

. 
. gen ftype=marginalf if treatedf!=.
(3,526,702 missing values generated)

. replace ftype=2 if alwaysf==1 & treatedf!=.
(9,865 real changes made)

. 
. gen fitype=marginalfi if treatedf!=.
(3,522,462 missing values generated)

. replace fitype=2 if alwaysfi==1 & treatedf!=.
(13,100 real changes made)

. 
. 
. *************************************************
. *****TABLE A15
. *************************************************
. 
. tab ftype fitype, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

           |              fitype
     ftype |         0          1          2 |     Total
-----------+---------------------------------+----------
         0 |     5,538      7,845      2,255 |    15,638 
           |     35.41      50.17      14.42 |    100.00 
-----------+---------------------------------+----------
         1 |     5,089     10,936      5,639 |    21,664 
           |     23.49      50.48      26.03 |    100.00 
-----------+---------------------------------+----------
         2 |       831      4,747      4,151 |     9,729 
           |      8.54      48.79      42.67 |    100.00 
-----------+---------------------------------+----------
     Total |    11,458     23,528     12,045 |    47,031 
           |     24.36      50.03      25.61 |    100.00 

. 
. 
. 
. 
. 
. 
. *************************************************
. *****TABLE A16
. *************************************************
. *Panel A
. 
. eststo clear

. 
. eststo all: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalf!=.
>  & neverfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      9,043
                                                F(14, 161)        =      80.41
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1111
                                                Root MSE          =     .45502

                                (Std. err. adjusted for 162 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0123253   .0132236     0.93   0.353    -.0137887    .0384393
     molincome |   .0052304   .0075539     0.69   0.490    -.0096872    .0201479
        female |   .0035026   .0092534     0.38   0.706    -.0147711    .0217763
           age |   .0083885   .0005025    16.69   0.000     .0073962    .0093808
               |
       moses1d |
            1  |    .131478   .0566402     2.32   0.022     .0196244    .2433316
            2  |   .0264351   .0275367     0.96   0.338    -.0279445    .0808147
            3  |    .121047   .0251764     4.81   0.000     .0713284    .1707656
            4  |   .0747956   .0207148     3.61   0.000     .0338879    .1157032
            5  |   -.012085   .0219106    -0.55   0.582    -.0553544    .0311843
            6  |   .0091163   .0330573     0.28   0.783    -.0561656    .0743982
            7  |  -.0277417   .0274526    -1.01   0.314    -.0819554     .026472
            9  |  -.0148763   .0307555    -0.48   0.629    -.0756126    .0458599
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1306751   .0150345     8.69   0.000     .1009849    .1603653
       foreign |  -.2206587   .0134199   -16.44   0.000    -.2471604   -.1941571
         _cons |  -.0687892   .0765537    -0.90   0.370     -.219968    .0823897
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalf==1 & neve
> rfi==1, cluster(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverf==1 & neverfi
> ==1, cluster(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverf==1 & 
> neverfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      3,447
                                                F(14, 138)        =      16.41
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0350
                                                Root MSE          =     .39134

                                (Std. err. adjusted for 139 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0072737   .0131129     0.55   0.580    -.0186545    .0332018
     molincome |  -.0052106   .0091295    -0.57   0.569    -.0232625    .0128412
        female |   .0056145   .0136835     0.41   0.682    -.0214418    .0326709
           age |   .0044483   .0006952     6.40   0.000     .0030736     .005823
               |
       moses1d |
            1  |   .1969673   .2155203     0.91   0.362    -.2291817    .6231163
            2  |    -.01359   .0301411    -0.45   0.653    -.0731881    .0460082
            3  |  -.0537784   .0453199    -1.19   0.237    -.1433896    .0358327
            4  |   -.006175   .0218274    -0.28   0.778    -.0493345    .0369845
            5  |  -.0597708   .0227673    -2.63   0.010    -.1047885    -.014753
            6  |  -.0790945    .035948    -2.20   0.029    -.1501745   -.0080144
            7  |   -.059988   .0371263    -1.62   0.108    -.1333981     .013422
            9  |  -.0677247   .0403301    -1.68   0.095    -.1474696    .0120202
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0588738   .0353867     1.66   0.098    -.0110964     .128844
       foreign |  -.1620364   .0146633   -11.05   0.000    -.1910301   -.1330426
         _cons |    .184218   .0850597     2.17   0.032     .0160291    .3524068
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalf==1 & neve
> rfi==1, cluster(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysf==1 & neverf
> i==1, cluster(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginalf: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if margin
> alf==1 & neverfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      4,827
                                                F(14, 138)        =      11.97
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0243
                                                Root MSE          =     .49094

                                (Std. err. adjusted for 139 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0300956   .0179923     1.67   0.097    -.0054807    .0656719
     molincome |   .0078075   .0109534     0.71   0.477    -.0138507    .0294658
        female |   .0161117   .0127914     1.26   0.210    -.0091807    .0414041
           age |    .008246   .0008517     9.68   0.000      .006562      .00993
               |
       moses1d |
            1  |   .2078993   .0721469     2.88   0.005      .065243    .3505556
            2  |   .0693877   .0453584     1.53   0.128    -.0202998    .1590751
            3  |   .1561857   .0426922     3.66   0.000     .0717703     .240601
            4  |   .1361947   .0362145     3.76   0.000     .0645877    .2078017
            5  |   .0595024   .0365235     1.63   0.106    -.0127157    .1317204
            6  |   .0754332   .0757774     1.00   0.321    -.0744017    .2252681
            7  |     .02855   .0445962     0.64   0.523    -.0596303    .1167303
            9  |   .0505648   .0573033     0.88   0.379    -.0627412    .1638709
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1168213   .0222515     5.25   0.000     .0728232    .1608193
       foreign |  -.1785707   .0961661    -1.86   0.065    -.3687202    .0115788
         _cons |  -.1654413   .1179999    -1.40   0.163     -.398763    .0678805
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginalf

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginalf

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysf==1 & neverf
> i==1, cluster(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverf==1 & neverfi
> ==1, cluster(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysf==1 
> & neverfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.
note: foreign omitted because of collinearity.

Linear regression                               Number of obs     =        769
                                                F(13, 89)         =       1.88
                                                Prob > F          =     0.0428
                                                R-squared         =     0.0254
                                                Root MSE          =     .46112

                                 (Std. err. adjusted for 90 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |  -.0991329   .0377846    -2.62   0.010      -.17421   -.0240558
     molincome |   .0150325   .0324653     0.46   0.644    -.0494753    .0795403
        female |  -.0336974   .0419919    -0.80   0.424    -.1171345    .0497396
           age |   .0025361   .0030135     0.84   0.402    -.0034517    .0085239
               |
       moses1d |
            1  |   -.052706   .2389658    -0.22   0.826    -.5275259    .4221138
            2  |  -.0249624   .2160283    -0.12   0.908     -.454206    .4042813
            3  |   .0269228   .2164778     0.12   0.901    -.4032139    .4570595
            4  |   .0609854   .2045315     0.30   0.766    -.3454144    .4673852
            5  |    .098958   .2276463     0.43   0.665    -.3533703    .5512863
            6  |  -.0522547   .3113738    -0.17   0.867    -.6709479    .5664384
            7  |   .0150678   .2254717     0.07   0.947    -.4329397    .4630753
            9  |  -.2574187   .3192239    -0.81   0.422    -.8917098    .3768724
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0410684   .0381405     1.08   0.284     -.034716    .1168529
       foreign |          0  (omitted)
         _cons |   .4155661   .4515158     0.92   0.360    -.4815863    1.312718
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 `"& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ "'

. local titles2 "& & Bottom 25\% & 25-75\% & Top 25\% \\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. esttab using spill_combvprop.tex, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01
> ) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prehead(\begin{table}[htbp]\centering ///
> \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} ///
> \caption{Spillovers by Combinations of Voting Propensity} ///
> \begin{tabular}{l*{4}{c}}\hline\hline) posthead("`header'" "`emptyrow'" `"`titles1'"' "`titles2'" "`numbers'" \\
>  \multicolumn{4}{c}{\textbf{Panel A: Low Propensity Youth in HH }} \\ "`emptyrow'")  ///
> refcat(treatedf "", nolabel below) postfoot(\hline) fragment nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)
(output written to spill_combvprop.tex)

. eststo clear

. 
. 
. *Panel B
. 
. 
. eststo all: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalf!=.
>  & marginalfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     18,184
                                                F(14, 192)        =     395.95
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1269
                                                Root MSE          =     .46727

                                (Std. err. adjusted for 193 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0153388   .0076807     2.00   0.047     .0001894    .0304881
     molincome |   .0323136   .0059026     5.47   0.000     .0206713    .0439559
        female |   .0421641   .0072947     5.78   0.000     .0277762    .0565521
           age |   .0091985   .0003262    28.20   0.000     .0085552    .0098418
               |
       moses1d |
            1  |   .2429712   .0304088     7.99   0.000     .1829929    .3029494
            2  |   .1057561   .0239736     4.41   0.000     .0584707    .1530416
            3  |   .1722523   .0171006    10.07   0.000     .1385231    .2059815
            4  |   .0924852   .0139305     6.64   0.000     .0650087    .1199618
            5  |   .0213472    .015653     1.36   0.174    -.0095268    .0522212
            6  |   .1455255   .0271974     5.35   0.000     .0918815    .1991695
            7  |   .0209431   .0246394     0.85   0.396    -.0276555    .0695417
            9  |   .0864441   .0335437     2.58   0.011     .0202827    .1526056
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1203836    .008216    14.65   0.000     .1041784    .1365887
       foreign |  -.2037927   .0261632    -7.79   0.000    -.2553969   -.1521885
         _cons |  -.3607177    .064116    -5.63   0.000    -.4871798   -.2342556
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalf==1 & marg
> inalfi==1, cluster(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverf==1 & margina
> lfi==1, cluster(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverf==1 & 
> marginalfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      4,318
                                                F(14, 156)        =      17.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0277
                                                Root MSE          =     .43938

                                (Std. err. adjusted for 157 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0266033    .013511     1.97   0.051    -.0000849    .0532915
     molincome |   .0151801   .0092942     1.63   0.104    -.0031785    .0335387
        female |   .0828852   .0114498     7.24   0.000     .0602685    .1055019
           age |   .0055167    .001036     5.33   0.000     .0034703    .0075631
               |
       moses1d |
            1  |   .2635254   .1404717     1.88   0.063    -.0139466    .5409975
            2  |   .1687096     .03645     4.63   0.000     .0967104    .2407088
            3  |   .1785625   .0479805     3.72   0.000     .0837873    .2733377
            4  |   .0888352   .0286488     3.10   0.002     .0322457    .1454248
            5  |   .0452313   .0299606     1.51   0.133    -.0139495     .104412
            6  |   .0852379   .0519972     1.64   0.103    -.0174715    .1879473
            7  |   .0467364   .0375816     1.24   0.216    -.0274982    .1209709
            9  |   .0521461   .0424143     1.23   0.221    -.0316343    .1359265
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1102557   .0319338     3.45   0.001     .0471772    .1733341
       foreign |  -.1343502   .0297161    -4.52   0.000     -.193048   -.0756524
         _cons |  -.1414588   .0954736    -1.48   0.140    -.3300465     .047129
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalf==1 & marg
> inalfi==1, cluster(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysf==1 & margin
> alfi==1, cluster(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginalf: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if margin
> alf==1 & marginalfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      9,412
                                                F(14, 172)        =      30.01
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0332
                                                Root MSE          =     .49185

                                (Std. err. adjusted for 173 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0106893   .0097015     1.10   0.272    -.0084601    .0298386
     molincome |   .0262245   .0092868     2.82   0.005     .0078937    .0445553
        female |   .0297893   .0127247     2.34   0.020     .0046726    .0549061
           age |   .0087583   .0005726    15.30   0.000     .0076281    .0098885
               |
       moses1d |
            1  |    .165798   .0440625     3.76   0.000     .0788251    .2527709
            2  |    .052848   .0342367     1.54   0.125    -.0147301    .1204261
            3  |    .127405   .0319013     3.99   0.000     .0644366    .1903734
            4  |   .0797789   .0284464     2.80   0.006     .0236298    .1359279
            5  |   .0209648   .0238678     0.88   0.381    -.0261466    .0680763
            6  |   .1454718    .034526     4.21   0.000     .0773226     .213621
            7  |  -.0035925   .0348025    -0.10   0.918    -.0722874    .0651025
            9  |   .0828512    .048611     1.70   0.090    -.0130997    .1788021
               |
     firstvote |          0  (omitted)
1.mohighschool |    .115205   .0175085     6.58   0.000     .0806459    .1497642
       foreign |  -.3119325   .0632062    -4.94   0.000    -.4366922   -.1871728
         _cons |  -.2517316    .107973    -2.33   0.021    -.4648544   -.0386088
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginalf

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginalf

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysf==1 & margin
> alfi==1, cluster(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverf==1 & margina
> lfi==1, cluster(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysf==1 
> & marginalfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      4,454
                                                F(13, 123)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0233
                                                Root MSE          =     .43508

                                (Std. err. adjusted for 124 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0125362    .016005     0.78   0.435    -.0191447    .0442171
     molincome |   .0521782   .0140162     3.72   0.000      .024434    .0799225
        female |   .0152068   .0114814     1.32   0.188    -.0075199    .0379335
           age |   .0045597   .0011691     3.90   0.000     .0022455     .006874
               |
       moses1d |
            1  |    .241123   .1070197     2.25   0.026      .029284    .4529619
            2  |   .1230455   .1071182     1.15   0.253    -.0889884    .3350793
            3  |    .156655   .0989758     1.58   0.116    -.0392615    .3525714
            4  |   .0952065   .1022333     0.93   0.354    -.1071581    .2975711
            5  |  -.0343673   .1135357    -0.30   0.763    -.2591043    .1903697
            6  |   .0531496   .1510969     0.35   0.726    -.2459374    .3522367
            7  |   .0670942   .1094714     0.61   0.541    -.1495977    .2837862
            9  |   .1049405   .1394371     0.75   0.453    -.1710667    .3809477
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0575316   .0221455     2.60   0.011      .013696    .1013672
       foreign |  -.8411047   .0181242   -46.41   0.000    -.8769804    -.805229
         _cons |  -.2435955   .2089382    -1.17   0.246    -.6571758    .1699848
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. esttab using spill_combvprop.tex, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01
> ) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{c}
> {\textbf{Panel B: Marginal Youth in HH}} \\ "`emptyrow'")  ///
> refcat(treatedf "", nolabel below) fragment append nonotes 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)
(output written to spill_combvprop.tex)

. 
. *Panel C
. 
. eststo clear

. 
. 
. 
. 
. 
. eststo all: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalf!=.
>  & alwaysfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      9,077
                                                F(14, 165)        =     175.27
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1155
                                                Root MSE          =     .45375

                                (Std. err. adjusted for 166 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |    .005364   .0115401     0.46   0.643    -.0174213    .0281493
     molincome |   .0274021   .0064459     4.25   0.000     .0146751    .0401291
        female |   .0216359   .0077912     2.78   0.006     .0062527    .0370191
           age |   .0082413   .0003356    24.56   0.000     .0075787    .0089038
               |
       moses1d |
            1  |   .1990264   .0355669     5.60   0.000     .1288016    .2692513
            2  |   .1180919   .0350908     3.37   0.001      .048807    .1873767
            3  |   .1696422   .0314139     5.40   0.000     .1076171    .2316673
            4  |   .0792837    .033409     2.37   0.019     .0133195     .145248
            5  |   .0054163   .0313909     0.17   0.863    -.0565633    .0673959
            6  |    .190108   .0374533     5.08   0.000     .1161584    .2640576
            7  |    .069566   .0448636     1.55   0.123    -.0190148    .1581468
            9  |   .1873346   .0581281     3.22   0.002     .0725639    .3021054
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0851263   .0150025     5.67   0.000     .0555047    .1147479
       foreign |  -.1611611   .0376172    -4.28   0.000    -.2354342   -.0868881
         _cons |  -.1599525   .0741574    -2.16   0.032    -.3063723   -.0135328
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalf==1 & alwa
> ysfi==1, cluster(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverf==1 & alwaysf
> i==1, cluster(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverf==1 & 
> alwaysfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      1,305
                                                F(14, 118)        =       2.15
                                                Prob > F          =     0.0137
                                                R-squared         =     0.0098
                                                Root MSE          =     .48022

                                (Std. err. adjusted for 119 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0210801   .0232927     0.91   0.367    -.0250457    .0672059
     molincome |    .015081   .0178855     0.84   0.401    -.0203372    .0504992
        female |   .0581619   .0269363     2.16   0.033     .0048207     .111503
           age |    .002511   .0019149     1.31   0.192     -.001281    .0063031
               |
       moses1d |
            1  |    .193687   .2047106     0.95   0.346    -.2116958    .5990697
            2  |  -.0135513   .0658157    -0.21   0.837    -.1438842    .1167816
            3  |  -.0673181   .1075397    -0.63   0.533    -.2802759    .1456397
            4  |   .0426634   .0444399     0.96   0.339    -.0453398    .1306666
            5  |   .0136889   .0405455     0.34   0.736    -.0666022    .0939801
            6  |   .0658691   .0694113     0.95   0.345    -.0715842    .2033223
            7  |   .1014946   .0657594     1.54   0.125    -.0287268    .2317161
            9  |   .0945209   .1021053     0.93   0.356    -.1076755    .2967172
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0233005   .0513202     0.45   0.651    -.0783275    .1249284
       foreign |  -.0772042   .0471113    -1.64   0.104    -.1704973     .016089
         _cons |   .0954492   .1859763     0.51   0.609    -.2728346     .463733
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if marginalf==1 & alwa
> ysfi==1, cluster(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysf==1 & always
> fi==1, cluster(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginalf: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if margin
> alf==1 & alwaysfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      3,924
                                                F(14, 148)        =      12.38
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0273
                                                Root MSE          =     .48943

                                (Std. err. adjusted for 149 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0118645   .0205353     0.58   0.564    -.0287157    .0524447
     molincome |   .0211283   .0107433     1.97   0.051    -.0001017    .0423584
        female |   .0369916   .0185209     2.00   0.048     .0003919    .0735912
           age |   .0071189   .0008471     8.40   0.000     .0054449     .008793
               |
       moses1d |
            1  |   .1663352   .0703097     2.37   0.019     .0273945    .3052759
            2  |   .0859883   .0572648     1.50   0.135     -.027174    .1991506
            3  |   .1309584    .059556     2.20   0.029     .0132685    .2486484
            4  |   .0621456   .0570877     1.09   0.278    -.0506667    .1749578
            5  |  -.0027065   .0505039    -0.05   0.957    -.1025085    .0970955
            6  |    .178469   .0560679     3.18   0.002      .067672    .2892661
            7  |   .0459725   .0698114     0.66   0.511    -.0919833    .1839283
            9  |   .2396276   .0765199     3.13   0.002      .088415    .3908402
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0674665   .0241426     2.79   0.006     .0197577    .1151752
       foreign |  -.0741877   .0670356    -1.11   0.270    -.2066583    .0582829
         _cons |  -.0338116   .1249423    -0.27   0.787    -.2807128    .2130896
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginalf

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginalf

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysf==1 & always
> fi==1, cluster(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if neverf==1 & alwaysf
> i==1, cluster(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treatedf molincome female age i.moses1d firstvote i.mohighschool foreign if alwaysf==1 
> & alwaysfi==1, cluster(kunta19)
note: firstvote omitted because of collinearity.
note: foreign omitted because of collinearity.

Linear regression                               Number of obs     =      3,848
                                                F(13, 113)        =       8.32
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0246
                                                Root MSE          =     .40226

                                (Std. err. adjusted for 114 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |  -.0055409   .0149391    -0.37   0.711     -.035138    .0240563
     molincome |   .0198664   .0136637     1.45   0.149    -.0072038    .0469366
        female |  -.0125037   .0134195    -0.93   0.353    -.0390901    .0140828
           age |   .0063964   .0010038     6.37   0.000     .0044077    .0083851
               |
       moses1d |
            1  |   .1735268    .075962     2.28   0.024     .0230324    .3240212
            2  |   .1556963    .084978     1.83   0.070    -.0126605     .324053
            3  |   .1832033   .0811052     2.26   0.026     .0225193    .3438873
            4  |   .0921521   .0773985     1.19   0.236    -.0611883    .2454925
            5  |   .0024584   .0919872     0.03   0.979    -.1797847    .1847015
            6  |   .1607175   .1019314     1.58   0.118     -.041227     .362662
            7  |   .0893829   .0876776     1.02   0.310    -.0843223    .2630881
            9  |   .0211811   .1405153     0.15   0.880    -.2572052    .2995673
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0395219   .0313952     1.26   0.211    -.0226776    .1017215
       foreign |          0  (omitted)
         _cons |   .0789259   .1735277     0.45   0.650    -.2648639    .4227156
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treatedf==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. 
. 
. esttab, keep (treatedf) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{c}
> {\textbf{Panel C: High Propensity Youth in HH}} \\ "`emptyrow'")  ///
> refcat(treatedf "", nolabel below) postfoot(\hline\hline \\ \end{tabular} \\ \end{table}) fragment append nonote
> s 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& & & &  \\ 
\multicolumn{4}{c}{\textbf{Panel
C:
High
Propensity
Youth
in
HH}}
\\
& & & &  \\ 
Treated in HH               0.005           0.021           0.012          -0.006   
                          (0.012)         (0.023)         (0.021)         (0.015)   

                                                                                    
\hline
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.631           0.347           0.561           0.797   
Observations                9,077           1,305           3,924           3,848   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                                -0.009           0.017          -0.027   
\phantom{se}                              (0.031)         (0.025)         (0.028)   
\hline\hline \\ \end{tabular} \\ \end{table}

. 
. 
. 
. 
. 
. 
. 
. *************************************************
. *****TABLE A9
. *************************************************
. 
. ****************************************************************************************
. 
. replace age=age+3 //replace age for age in 2022
(3,562,582 real changes made)

. 
. rename svatva_k income

. 
. reg voted22 treatedf molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     36,876
                                                F(14, 259)        =     612.45
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1407
                                                Root MSE          =     .46358

                                (Std. err. adjusted for 260 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
      treatedf |   .0130222   .0055449     2.35   0.020     .0021033     .023941
     molincome |   .0248605   .0040409     6.15   0.000     .0169033    .0328177
        female |   .0095634   .0045088     2.12   0.035     .0006849     .018442
           age |   .0086664   .0002753    31.48   0.000     .0081243    .0092085
       foreign |  -.2464759   .0128641   -19.16   0.000    -.2718074   -.2211443
               |
       moses1d |
            1  |   .2246265   .0207849    10.81   0.000     .1836976    .2655553
            2  |   .0959936   .0167937     5.72   0.000     .0629242    .1290631
            3  |   .1767397     .01162    15.21   0.000      .153858    .1996215
            4  |   .0857361   .0117858     7.27   0.000     .0625278    .1089443
            5  |   .0027727   .0104861     0.26   0.792    -.0178761    .0234216
            6  |   .1327165   .0162459     8.17   0.000     .1007256    .1647075
            7  |   .0146583   .0174838     0.84   0.403    -.0197702    .0490868
            9  |   .0622016   .0236856     2.63   0.009     .0155608    .1088425
               |
     firstvote |          0  (omitted)
1.mohighschool |   .1455762   .0079108    18.40   0.000     .1299985    .1611539
         _cons |  -.2741966    .048991    -5.60   0.000    -.3706679   -.1777253
--------------------------------------------------------------------------------

. keep if e(sample)==1
(3,537,408 observations deleted)

. 
. label variable female "Female"

. label variable age "Age"

. label variable mohighschool "High School Background"

. label variable income "Taxable Income"

. label variable foreign "Immigration Background"

. 
. eststo clear

. eststo s1: estpost tabstat female age mohighschool income foreign if neverf==1,statistics(mean sd) columns(stati
> stics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .4497821   .4974989 
         age |  30.44967   10.03643 
mohighschool |  .0863834   .2809447 
      income |  21557.95   15509.72 
     foreign |  .1558824   .3627635 

. eststo s2: estpost tabstat female age mohighschool income foreign if marginalf==1,statistics(mean sd) columns(st
> atistics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .4831718   .4997303 
         age |  44.40987   13.85975 
mohighschool |  .3747413   .4840692 
      income |  31214.75   19080.58 
     foreign |  .0062085    .078551 

. eststo s3: estpost tabstat female age mohighschool income foreign if alwaysf==1,statistics(mean sd) columns(stat
> istics)

Summary statistics: mean sd
     for variables: female age mohighschool income foreign

             |   e(mean)      e(sd) 
-------------+----------------------
      female |  .5717242   .4948558 
         age |   54.8144   6.921264 
mohighschool |  .7990415   .4007389 
      income |  52178.04   29122.75 
     foreign |  .0001089   .0104365 

. 
. 
. esttab using summary_spill.tex, cells(mean(fmt(2)) sd(fmt(2) par)) unstack nonote nonumber noobs stats(N, fmt(a)
>  labels("Observations")) mtitles("Low Propensity" "Marginal Voters" "High Propensity") replace
(output written to summary_spill.tex)

. 
. *************************************************
. *****TABLE A8
. *************************************************
. 
. 
. eststo clear

. 
. eststo all: estpost sum  female age mohighschool income foreign if voted22!=.

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
      female |     36876      36876   .4972069    .249999    .499999          0          1      18335 
         age |     36876      36876   43.46762   208.6055   14.44318         18         87    1602912 
mohighschool |     36876      36876   .4085313     .24164   .4915689          0          1      15065 
      income |     36442      36442   34089.86   5.81e+08   24100.36          0     143900   1.24e+09 
     foreign |     36876      36876    .042087   .0403168   .2007904          0          1       1552 

. eststo control: estpost sum  female age mohighschool income foreign if voted22!=. & treatedf==0

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
      female |     14662      14662    .496999    .250008    .500008          0          1       7287 
         age |     14662      14662   43.44339   209.3817   14.47003         18         81     636967 
mohighschool |     14662      14662   .4099032   .2418991   .4918323          0          1       6010 
      income |     14487      14487   34233.64   5.94e+08   24365.42          0     143900   4.96e+08 
     foreign |     14662      14662   .0416724   .0399385   .1998462          0          1        611 

. eststo treated: estpost sum  female age mohighschool income foreign if voted22!=. & treatedf==1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
      female |     22214      22214    .497344   .2500042   .5000042          0          1      11048 
         age |     22214      22214   43.48361   208.1019   14.42574         18         87     965945 
mohighschool |     22214      22214   .4076258   .2414779    .491404          0          1       9055 
      income |     21955      21955   33994.99   5.72e+08   23923.93          0     143900   7.46e+08 
     foreign |     22214      22214   .0423607   .0405681   .2014152          0          1        941 

. eststo difference: estpost ttest  female age mohighschool income foreign if voted22!=., by(treatedf)

             |      e(b)   e(count)      e(se)       e(t)    e(df_t)     e(p_l)       e(p)     e(p_u)     e(N_1) 
-------------+---------------------------------------------------------------------------------------------------
      female |  -.000345      36876   .0053203  -.0648406      36874   .4741506   .9483013   .5258494      14662 
         age | -.0402229      36876   .1536846  -.2617235      36874    .396768   .7935361    .603232      14662 
mohighschool |  .0022773      36876   .0052306   .4353863      36874   .6683577   .6632846   .3316423      14662 
      income |  238.6542      36442   257.9702   .9251232      36440    .822546   .3549079    .177454      14487 
     foreign | -.0006883      36876   .0021365  -.3221679      36874   .3736637   .7473273   .6263363      14662 

             |   e(mu_1)     e(N_2)    e(mu_2) 
-------------+---------------------------------
      female |   .496999      22214    .497344 
         age |  43.44339      22214   43.48361 
mohighschool |  .4099032      22214   .4076258 
      income |  34233.64      21955   33994.99 
     foreign |  .0416724      22214   .0423607 

. 
. 
. esttab all control treated difference using balance_spill.tex, replace mtitle("All" "Control" "Treated" "Differe
> nce") ///
> cells( ///
> mean(pattern(1 1 1 0) fmt(%9.3f)) & b(star pattern(0 0 0 1) fmt(%9.3f)) ///
> sd(par pattern(1 1 1 0) fmt(%9.3f)) & se(par pattern(0 0 0 1) fmt(%9.3f))) ///
> nonote nodepvars collabels("" "" "" "") nostar
(output written to balance_spill.tex)

. 
end of do-file

. do "$home\dofiles\TableA14.do"

. ****************************************************************************************************************
> ****
. *****Do-file for Table A14
. *****Who is mobilized to vote by short text messages? Evidence from a nationwide field experiment with young vot
> ers
. ****************************************************************************************************************
> ****
. *****Last edited 24/6/5
. ****************************************************************************************************************
> ****
. *****Ado packages needed: estout
. ****************************************************************************************************************
> ****
. 
. clear all

. 
. *Programs to calculate group differences with standard errors and stars
. capture program drop, myrepost

. program myrepost, eclass
  1. ereturn repost b=`1'
  2. ereturn repost V=`2'
  3. ereturn scalar df_r=`3'
  4. end

. 
. capture program drop mystars

. program mystars, eclass
  1. local d_stars=string(`1', "%9.3f")
  2. local pval=ttail(`3',abs(`1'/`2'))*2
  3. 
. if `pval'<=0.01 {
  4. local d_stars="`d_stars'"+"***"         
  5. }
  6. 
. if `pval'>0.01 & `pval'<=0.05  {
  7. local d_stars="`d_stars'"+"**"          
  8. }
  9. 
. if `pval'>0.05 & `pval'<=0.1  {
 10. local d_stars="`d_stars'"+"*"           
 11. }
 12. 
. local se_stars=string(`2', "%9.3f")
 13. 
. local se_stars="("+"`se_stars'"+")"
 14. 
. ereturn local d_stars="`d_stars'"
 15. ereturn local se_stars="`se_stars'"
 16. 
. 
. end

. 
. *Use data
. use \data\dataforanalysis230522_v2.dta, clear

. *Set basecategory
. fvset base 8 moses1d

. 
. *************************************************
. *****TABLE A14
. *************************************************
. 
. 
. 
. 
. 
. eststo clear

. 
. label variable treated "Treated"

. 
. 
. 
. eststo pooled: reg voted22adv treated molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(k
> unta19)

Linear regression                               Number of obs     =     49,679
                                                F(15, 289)        =      60.97
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0363
                                                Root MSE          =     .34056

                                (Std. err. adjusted for 290 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
    voted22adv | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0043481   .0025786     1.69   0.093    -.0007271    .0094233
     molincome |   -.001254   .0034939    -0.36   0.720    -.0081307    .0056228
        female |   .0687401   .0060661    11.33   0.000     .0568006    .0806795
           age |   .0034528   .0007313     4.72   0.000     .0020135    .0048922
       foreign |  -.0502974   .0058616    -8.58   0.000    -.0618343   -.0387605
               |
       moses1d |
            1  |   .0632953   .0140781     4.50   0.000     .0355867     .091004
            2  |   .0161024   .0098658     1.63   0.104    -.0033156    .0355203
            3  |   .0580863   .0087478     6.64   0.000     .0408688    .0753038
            4  |   .0071956   .0060475     1.19   0.235    -.0047072    .0190984
            5  |  -.0132162   .0075018    -1.76   0.079    -.0279813    .0015489
            6  |   .0320781   .0084548     3.79   0.000     .0154372     .048719
            7  |   .0283409   .0101886     2.78   0.006     .0082876    .0483941
            9  |  -.0014426   .0118717    -0.12   0.903    -.0248085    .0219234
               |
     firstvote |   .0216445   .0093557     2.31   0.021     .0032305    .0400585
1.mohighschool |   .0802532   .0076482    10.49   0.000     .0651999    .0953065
         _cons |   -.001891   .0353749    -0.05   0.957     -.071516    .0677341
--------------------------------------------------------------------------------

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store pooled

. qui: mean voted22adv if treated==0 & e(sample)==1

. scalar umean1s=r(table)[1,1]

. local umean1: di %9.3f scalar(umean1s) 

. 
. eststo neutral: reg voted22adv treated molincome female age foreign i.moses1d firstvote i.mohighschool if treate
> d1!=., cluster(kunta19)

Linear regression                               Number of obs     =     29,799
                                                F(15, 278)        =      76.04
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0377
                                                Root MSE          =     .34051

                                (Std. err. adjusted for 279 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
    voted22adv | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0089022   .0034341     2.59   0.010      .002142    .0156624
     molincome |   .0024589   .0033904     0.73   0.469    -.0042153    .0091331
        female |   .0711399   .0069181    10.28   0.000     .0575215    .0847584
           age |   .0036485   .0007918     4.61   0.000     .0020898    .0052073
       foreign |  -.0484706   .0063941    -7.58   0.000    -.0610576   -.0358835
               |
       moses1d |
            1  |   .0603398   .0193667     3.12   0.002     .0222158    .0984639
            2  |   .0072327   .0130181     0.56   0.579    -.0183938    .0328593
            3  |   .0524914   .0119072     4.41   0.000     .0290516    .0759312
            4  |   .0011095   .0085235     0.13   0.897    -.0156693    .0178884
            5  |  -.0237967    .009198    -2.59   0.010    -.0419032   -.0056901
            6  |    .024717    .013228     1.87   0.063    -.0013227    .0507568
            7  |   .0266414      .0141     1.89   0.060     -.001115    .0543978
            9  |  -.0049077   .0126095    -0.39   0.697    -.0297299    .0199145
               |
     firstvote |   .0285924   .0134755     2.12   0.035     .0020655    .0551194
1.mohighschool |   .0796398   .0096122     8.29   0.000     .0607179    .0985618
         _cons |  -.0382814   .0361924    -1.06   0.291    -.1095273    .0329645
--------------------------------------------------------------------------------

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store neutral

. qui: mean voted22adv if treated==0 & e(sample)==1

. scalar umean2s=r(table)[1,1]

. local umean2: di %9.3f scalar(umean2s) 

. 
. 
. eststo expressive: reg voted22adv treated molincome female age foreign i.moses1d firstvote i.mohighschool if tre
> ated2!=., cluster(kunta19)

Linear regression                               Number of obs     =     29,806
                                                F(15, 280)        =      42.58
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0376
                                                Root MSE          =      .3386

                                (Std. err. adjusted for 281 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
    voted22adv | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0024063   .0036645     0.66   0.512    -.0048072    .0096198
     molincome |   .0008502   .0032787     0.26   0.796    -.0056039    .0073043
        female |   .0695217   .0080172     8.67   0.000       .05374    .0853033
           age |   .0038134    .000967     3.94   0.000     .0019099    .0057169
       foreign |    -.04967    .006547    -7.59   0.000    -.0625575   -.0367824
               |
       moses1d |
            1  |   .0638381   .0176379     3.62   0.000     .0291184    .0985577
            2  |   .0079946   .0119334     0.67   0.503    -.0154961    .0314852
            3  |   .0443931   .0116023     3.83   0.000     .0215544    .0672319
            4  |  -.0014303   .0080305    -0.18   0.859    -.0172382    .0143775
            5  |  -.0197584   .0100378    -1.97   0.050    -.0395175    6.42e-07
            6  |   .0261599   .0099521     2.63   0.009     .0065696    .0457503
            7  |   .0244876   .0125441     1.95   0.052    -.0002052    .0491803
            9  |  -.0204064   .0191914    -1.06   0.289    -.0581842    .0173713
               |
     firstvote |   .0323061   .0126274     2.56   0.011     .0074494    .0571628
1.mohighschool |    .084088   .0096995     8.67   0.000     .0649949    .1031812
         _cons |  -.0251481   .0353928    -0.71   0.478    -.0948178    .0445217
--------------------------------------------------------------------------------

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store expressive

. qui: mean voted22adv if treated==0 & e(sample)==1

. scalar umean3s=r(table)[1,1]

. local umean3: di %9.3f scalar(umean3s) 

. 
. 
. 
. eststo informative: reg voted22adv treated molincome female age foreign i.moses1d firstvote i.mohighschool if tr
> eated3!=., cluster(kunta19)

Linear regression                               Number of obs     =     29,832
                                                F(15, 276)        =      36.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0359
                                                Root MSE          =     .33885

                                (Std. err. adjusted for 277 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
    voted22adv | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |     .00178   .0040645     0.44   0.662    -.0062214    .0097815
     molincome |  -.0012847   .0038355    -0.33   0.738    -.0088352    .0062658
        female |   .0670332   .0078353     8.56   0.000     .0516086    .0824577
           age |   .0038651   .0009514     4.06   0.000     .0019922    .0057379
       foreign |  -.0538827   .0071305    -7.56   0.000    -.0679197   -.0398457
               |
       moses1d |
            1  |    .048669   .0178776     2.72   0.007     .0134753    .0838627
            2  |    .012118   .0102233     1.19   0.237    -.0080077    .0322436
            3  |     .05535   .0108665     5.09   0.000     .0339583    .0767418
            4  |  -.0022481   .0088109    -0.26   0.799    -.0195932    .0150969
            5  |  -.0218012   .0092024    -2.37   0.019    -.0399171   -.0036853
            6  |   .0309994   .0112227     2.76   0.006     .0089063    .0530924
            7  |   .0219888    .012547     1.75   0.081    -.0027112    .0466888
            9  |  -.0191455   .0229702    -0.83   0.405    -.0643646    .0260736
               |
     firstvote |    .023608   .0111737     2.11   0.036     .0016114    .0456045
1.mohighschool |   .0759822   .0097536     7.79   0.000     .0567813    .0951831
         _cons |  -.0008356   .0406085    -0.02   0.984    -.0807774    .0791062
--------------------------------------------------------------------------------

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store informative

. qui: mean voted22adv if treated==0 & e(sample)==1

. scalar umean4s=r(table)[1,1]

. local umean4: di %9.3f scalar(umean4s) 

. 
. 
. ****************************************************************************************************************
> ****************************************************
. local titles1 `"Treatment: & Pooled & "Neutral" & "Expressive" & "Informative"\\ "'

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. esttab using adv_voting.tex, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
>  mlabels(none) nonumbers prehead(\begin{table}[htbp]\centering ///
> \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} ///
> \caption{Advance and Election Day Voting} ///
> \begin{tabular}{l*{4}{c}}\hline\hline) posthead(`"`titles1'"' "`titles2'" "`numbers'" \\ \multicolumn{4}{l}{\tex
> tbf{Outcome:} Voted in Advance} \\ "`emptyrow'")  ///
> refcat(treated "Untreated $\bar{Y}$ &`umean1'&`umean2'&`umean3'&`umean4'\\ %", nolabel below) ///
> fragment nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)
(output written to adv_voting.tex)

. eststo clear

. 
. qui: reg voted22adv treated molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22elday treated molincome female age foreign i.moses1d firstvote i.mohighschool, cluster(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. 
. eststo pooled: reg voted22elday treated molincome female age foreign i.moses1d firstvote i.mohighschool, cluster
> (kunta19)

Linear regression                               Number of obs     =     49,679
                                                F(15, 289)        =      94.73
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0196
                                                Root MSE          =     .37526

                                (Std. err. adjusted for 290 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
  voted22elday | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0046669   .0032231     1.45   0.149    -.0016769    .0110107
     molincome |    .003951   .0038166     1.04   0.301    -.0035608    .0114629
        female |   .0357077   .0034717    10.29   0.000     .0288748    .0425407
           age |   .0041118   .0007091     5.80   0.000     .0027162    .0055074
       foreign |  -.0924204   .0050079   -18.45   0.000    -.1022771   -.0825638
               |
       moses1d |
            1  |   .1264952   .0219549     5.76   0.000     .0832835     .169707
            2  |   .0560907    .008733     6.42   0.000     .0389024    .0732791
            3  |   .0701412   .0066908    10.48   0.000     .0569724      .08331
            4  |   .0386584   .0060046     6.44   0.000     .0268402    .0504767
            5  |   .0189571   .0059393     3.19   0.002     .0072673     .030647
            6  |   .0400026   .0082399     4.85   0.000     .0237848    .0562204
            7  |   .0423797   .0091524     4.63   0.000     .0243658    .0603936
            9  |   .0086249   .0108929     0.79   0.429    -.0128145    .0300644
               |
     firstvote |   .1017831   .0088704    11.47   0.000     .0843243    .1192418
1.mohighschool |   .0517074    .005043    10.25   0.000     .0417818     .061633
         _cons |  -.0351583   .0384493    -0.91   0.361    -.1108344    .0405178
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store pooled

. 
. qui: mean voted22elday if treated==0 & e(sample)==1

. scalar umean1s=r(table)[1,1]

. local umean1: di %9.3f scalar(umean1s) 

. 
. qui: reg voted22adv treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated1!=., clus
> ter(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22elday treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated1!=., cl
> uster(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo neutral: reg voted22elday treated molincome female age foreign i.moses1d firstvote i.mohighschool if trea
> ted1!=., cluster(kunta19)

Linear regression                               Number of obs     =     29,799
                                                F(15, 278)        =      58.30
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0186
                                                Root MSE          =       .375

                                (Std. err. adjusted for 279 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
  voted22elday | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0075481   .0056353     1.34   0.182    -.0035451    .0186413
     molincome |   .0039206   .0039399     1.00   0.321    -.0038352    .0116764
        female |   .0373984   .0038095     9.82   0.000     .0298993    .0448975
           age |   .0037101   .0008503     4.36   0.000     .0020362     .005384
       foreign |  -.0843674   .0059035   -14.29   0.000    -.0959887   -.0727462
               |
       moses1d |
            1  |    .116788   .0239602     4.87   0.000     .0696215    .1639545
            2  |   .0615942   .0115821     5.32   0.000     .0387944    .0843939
            3  |   .0672631    .011506     5.85   0.000     .0446131    .0899131
            4  |   .0417174   .0079151     5.27   0.000     .0261362    .0572986
            5  |   .0238389   .0076166     3.13   0.002     .0088454    .0388325
            6  |   .0380458   .0090151     4.22   0.000     .0202993    .0557923
            7  |   .0446196    .011979     3.72   0.000     .0210384    .0682007
            9  |   .0125767   .0128724     0.98   0.329     -.012763    .0379165
               |
     firstvote |   .0952271   .0132336     7.20   0.000     .0691764    .1212778
1.mohighschool |   .0536676     .00628     8.55   0.000     .0413052    .0660301
         _cons |  -.0296833   .0430002    -0.69   0.491    -.1143307    .0549641
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store neutral

. qui: mean voted22elday if treated==0 & e(sample)==1

. scalar umean2s=r(table)[1,1]

. local umean2: di %9.3f scalar(umean2s) 

. 
. qui: reg voted22adv treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated2!=., clus
> ter(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22elday treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated2!=., cl
> uster(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo expressive: reg voted22elday treated molincome female age foreign i.moses1d firstvote i.mohighschool if t
> reated2!=., cluster(kunta19)

Linear regression                               Number of obs     =     29,806
                                                F(15, 280)        =      44.29
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0193
                                                Root MSE          =      .3749

                                (Std. err. adjusted for 281 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
  voted22elday | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0066316   .0036884     1.80   0.073     -.000629    .0138921
     molincome |   .0038111   .0041103     0.93   0.355    -.0042798    .0119021
        female |   .0305549   .0042613     7.17   0.000     .0221668    .0389431
           age |    .003466   .0009125     3.80   0.000     .0016698    .0052622
       foreign |  -.0897205   .0058486   -15.34   0.000    -.1012333   -.0782077
               |
       moses1d |
            1  |   .1271399   .0240738     5.28   0.000     .0797513    .1745285
            2  |   .0593067    .012328     4.81   0.000     .0350393    .0835741
            3  |   .0748944   .0094481     7.93   0.000     .0562959    .0934928
            4  |   .0425545   .0082666     5.15   0.000     .0262818    .0588271
            5  |   .0198132   .0074333     2.67   0.008     .0051809    .0344454
            6  |   .0446037   .0104557     4.27   0.000     .0240219    .0651854
            7  |   .0462176   .0134484     3.44   0.001     .0197447    .0726904
            9  |   .0038045   .0167033     0.23   0.820    -.0290754    .0366845
               |
     firstvote |   .0937096   .0128799     7.28   0.000     .0683559    .1190634
1.mohighschool |   .0521071   .0070479     7.39   0.000     .0382335    .0659808
         _cons |  -.0206795   .0467511    -0.44   0.659    -.1127078    .0713488
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store expressive

. qui: mean voted22elday if treated==0 & e(sample)==1

. scalar umean3s=r(table)[1,1]

. local umean3: di %9.3f scalar(umean3s) 

. 
. 
. qui: reg voted22adv treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated3!=., clus
> ter(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22elday treated molincome female age foreign i.moses1d firstvote i.mohighschool if treated3!=., cl
> uster(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo informative: reg voted22elday treated molincome female age foreign i.moses1d firstvote i.mohighschool if 
> treated3!=., cluster(kunta19)

Linear regression                               Number of obs     =     29,832
                                                F(15, 276)        =      52.47
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0191
                                                Root MSE          =     .37314

                                (Std. err. adjusted for 277 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
  voted22elday | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |  -.0001111   .0037644    -0.03   0.976    -.0075217    .0072994
     molincome |   .0039156   .0041269     0.95   0.344    -.0042087    .0120399
        female |   .0351921    .003646     9.65   0.000     .0280146    .0423697
           age |   .0046374   .0008172     5.67   0.000     .0030286    .0062462
       foreign |  -.0792706   .0074337   -10.66   0.000    -.0939045   -.0646366
               |
       moses1d |
            1  |   .1315374   .0265148     4.96   0.000     .0793406    .1837343
            2  |   .0540266   .0097565     5.54   0.000     .0348199    .0732332
            3  |   .0645958   .0088794     7.27   0.000     .0471158    .0820759
            4  |   .0402107   .0074974     5.36   0.000     .0254513    .0549701
            5  |   .0153555   .0081739     1.88   0.061    -.0007357    .0314467
            6  |   .0274028   .0100419     2.73   0.007     .0076344    .0471713
            7  |   .0469997   .0117336     4.01   0.000      .023901    .0700983
            9  |   .0060511   .0173164     0.35   0.727    -.0280378      .04014
               |
     firstvote |   .1069747   .0126829     8.43   0.000     .0820072    .1319423
1.mohighschool |   .0518241   .0058255     8.90   0.000      .040356    .0632922
         _cons |   -.045168   .0426234    -1.06   0.290    -.1290763    .0387403
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store informative

. qui: mean voted22elday if treated==0 & e(sample)==1

. scalar umean4s=r(table)[1,1]

. local umean4: di %9.3f scalar(umean4s) 

. 
. 
. 
. esttab, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls "  "N Observations" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{l}{\text
> bf{Outcome:} Voted on Election Day} \\ "`emptyrow'")  ///
> refcat(treated "Untreated $\bar{Y}$ &`umean1'&`umean2'&`umean3'&`umean4'\\ %", nolabel below) postfoot(\hline\hl
> ine \\ \end{tabular} \\ \end{table}) fragment append nonotes 
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& & & &  \\ 
\multicolumn{4}{l}{\textbf{Outcome:}
Voted
on
Election
Day}
\\
& & & &  \\ 
Treated                     0.005           0.008           0.007*         -0.000   
                          (0.003)         (0.006)         (0.004)         (0.004)   

Untreated $\b..17~17                                                                
\hline
Controls                      Yes             Yes             Yes             Yes   
Observations               49,679          29,799          29,806          29,832   
Differences                -0.000           0.001          -0.004           0.002   
\phantom{se}              (0.004)         (0.007)         (0.005)         (0.006)   
\hline\hline \\ \end{tabular} \\ \end{table}

. 
end of do-file

. do "$home\dofiles\TableA17.do"

. ****************************************************************************************************************
> ****
. *****Do-file for Table A17
. *****Who is mobilized to vote by short text messages? Evidence from a nationwide field experiment with young vot
> ers
. ****************************************************************************************************************
> ****
. *****Last edited 24/6/5
. ****************************************************************************************************************
> ****
. *****Ado packages needed: estout
. ****************************************************************************************************************
> ****
. 
. 
. clear all

. 
. *Programs to calculate group differences with standard errors and stars
. capture program drop, myrepost

. program myrepost, eclass
  1. ereturn repost b=`1'
  2. ereturn repost V=`2'
  3. ereturn scalar df_r=`3'
  4. end

. 
. capture program drop mystars

. program mystars, eclass
  1. local d_stars=string(`1', "%9.3f")
  2. local pval=ttail(`3',abs(`1'/`2'))*2
  3. 
. if `pval'<=0.01 {
  4. local d_stars="`d_stars'"+"***"         
  5. }
  6. 
. if `pval'>0.01 & `pval'<=0.05  {
  7. local d_stars="`d_stars'"+"**"          
  8. }
  9. 
. if `pval'>0.05 & `pval'<=0.1  {
 10. local d_stars="`d_stars'"+"*"           
 11. }
 12. 
. local se_stars=string(`2', "%9.3f")
 13. 
. local se_stars="("+"`se_stars'"+")"
 14. 
. ereturn local d_stars="`d_stars'"
 15. ereturn local se_stars="`se_stars'"
 16. 
. 
. end

. 
. *Use data
. use \data\dataforanalysis230522_v2.dta, clear

. *Set basecategory
. fvset base 8 moses1d

. 
. 
. *************************************************
. *****TABLE A17
. *************************************************
. 
. 
. label variable treated "Treated"

. 
. *Generate voting propensity groups for direct effects
. sum pvote_mother if voted22!=.  & treated!=. & female!=., detail

                         Pr(voted22)
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0714104        .023837
 5%      .118203       .0244854
10%     .1504558       .0245744       Obs              49,458
25%     .2063651       .0256294       Sum of wgt.      49,458

50%     .2950955                      Mean           .3090878
                        Largest       Std. dev.      .1320126
75%     .4018736       .8301099
90%     .4902918       .8364667       Variance       .0174273
95%     .5421607       .8550987       Skewness       .4419521
99%     .6177514       .8821674       Kurtosis       2.669571

. gen marginal=.
(3,574,284 missing values generated)

. replace marginal=1 if pvote_mother>=r(p25) & pvote_mother<r(p75)
(1,616,830 real changes made)

. replace marginal=0 if pvote_mother<r(p25) | (pvote_mother>=r(p75) & pvote_mother!=.)
(1,665,387 real changes made)

. 
. gen never=.
(3,574,284 missing values generated)

. replace never=1 if pvote_mother<r(p25)
(857,917 real changes made)

. replace never=0 if pvote_mother>=r(p25) & pvote_mother!=.
(2,424,300 real changes made)

. 
. gen always=.
(3,574,284 missing values generated)

. replace always=1 if pvote_mother>=r(p75) & pvote_mother!=.
(807,470 real changes made)

. replace always=0 if pvote_mother<r(p75)
(2,474,747 real changes made)

. 
. 
. 
. eststo clear

. 
. eststo all: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal!=. &
>  voted21==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     17,594
                                                F(14, 225)        =     102.48
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0317
                                                Root MSE          =     .47991

                                (Std. err. adjusted for 226 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |    .027612   .0070027     3.94   0.000     .0138127    .0414113
     molincome |  -.0089818   .0056721    -1.58   0.115    -.0201591    .0021954
        female |   .0791381   .0075102    10.54   0.000     .0643387    .0939375
           age |   .0131988   .0012203    10.82   0.000     .0107941    .0156035
               |
       moses1d |
            1  |   .0959691    .033185     2.89   0.004      .030576    .1613622
            2  |   .0222145   .0184693     1.20   0.230    -.0141805    .0586095
            3  |   .0528041   .0169287     3.12   0.002     .0194451    .0861631
            4  |   .0145605   .0135524     1.07   0.284    -.0121453    .0412663
            5  |  -.0227734   .0175942    -1.29   0.197    -.0574438    .0118969
            6  |  -.0021502   .0242806    -0.09   0.930    -.0499967    .0456963
            7  |    .034074   .0266057     1.28   0.202    -.0183543    .0865022
            9  |  -.0184601   .0223499    -0.83   0.410     -.062502    .0255818
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0781536   .0073163    10.68   0.000     .0637364    .0925708
       foreign |   -.223825   .0282713    -7.92   0.000    -.2795353   -.1681146
         _cons |   .3022035   .0618099     4.89   0.000     .1804032    .4240038
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1 & voted2
> 1==1, cluster(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1 & voted21==
> 1, cluster(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1 & vo
> ted21==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      2,871
                                                F(14, 148)        =       7.31
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0230
                                                Root MSE          =     .49458

                                (Std. err. adjusted for 149 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0617159   .0185221     3.33   0.001     .0251139    .0983179
     molincome |  -.0048091   .0132466    -0.36   0.717    -.0309859    .0213677
        female |   .0029497   .0268743     0.11   0.913    -.0501573    .0560567
           age |   .0157846   .0029811     5.29   0.000     .0098935    .0216757
               |
       moses1d |
            1  |   .0947255   .2084025     0.45   0.650    -.3171034    .5065544
            2  |  -.0260817   .0494455    -0.53   0.599    -.1237921    .0716286
            3  |   .0758594   .0645316     1.18   0.242    -.0516629    .2033817
            4  |   .0435326   .0341195     1.28   0.204    -.0238916    .1109569
            5  |  -.0044937   .0344462    -0.13   0.896    -.0725637    .0635763
            6  |  -.0265413   .0430339    -0.62   0.538    -.1115816     .058499
            7  |   .0074753   .0718403     0.10   0.917    -.1344899    .1494404
            9  |  -.0012393   .0504239    -0.02   0.980    -.1008831    .0984046
               |
     firstvote |          0  (omitted)
1.mohighschool |   -.028544   .0430033    -0.66   0.508    -.1135237    .0564358
       foreign |  -.1398978   .0359026    -3.90   0.000    -.2108457   -.0689499
         _cons |   .1362454    .144597     0.94   0.348    -.1494959    .4219868
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1 & voted2
> 1==1, cluster(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1 & voted21=
> =1, cluster(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginal: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal
> ==1 & voted21==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      8,769
                                                F(14, 182)        =       9.37
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0094
                                                Root MSE          =     .48848

                                (Std. err. adjusted for 183 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0304909   .0116309     2.62   0.009     .0075421    .0534396
     molincome |  -.0143718   .0093138    -1.54   0.125    -.0327488    .0040051
        female |   .0415925   .0153508     2.71   0.007     .0113041    .0718809
           age |   .0111728    .001946     5.74   0.000     .0073331    .0150125
               |
       moses1d |
            1  |   .0374963    .045171     0.83   0.408    -.0516299    .1266225
            2  |   .0146193   .0238577     0.61   0.541    -.0324538    .0616925
            3  |    .014541   .0235259     0.62   0.537    -.0318775    .0609595
            4  |  -.0047448   .0181115    -0.26   0.794    -.0404802    .0309907
            5  |  -.0249554   .0225278    -1.11   0.269    -.0694047     .019494
            6  |  -.0377209   .0305855    -1.23   0.219    -.0980686    .0226269
            7  |   .0162889   .0370174     0.44   0.660    -.0567494    .0893273
            9  |   .0412656   .0479582     0.86   0.391      -.05336    .1358911
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0354018   .0097986     3.61   0.000     .0160685    .0547352
       foreign |  -.1978758   .0468702    -4.22   0.000    -.2903546   -.1053971
         _cons |   .4515839   .0997729     4.53   0.000     .2547235    .6484443
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginal

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginal

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1 & voted21=
> =1, cluster(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1 & voted21==
> 1, cluster(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1 & 
> voted21==1, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      5,954
                                                F(13, 159)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0130
                                                Root MSE          =     .45613

                                (Std. err. adjusted for 160 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0093597   .0096221     0.97   0.332     -.009644    .0283633
     molincome |  -.0074832   .0064522    -1.16   0.248    -.0202264    .0052599
        female |   .0590955   .0120247     4.91   0.000     .0353469    .0828442
           age |    .012264   .0016979     7.22   0.000     .0089106    .0156175
               |
       moses1d |
            1  |  -.0093183    .042425    -0.22   0.826    -.0931076     .074471
            2  |  -.0751471   .0353832    -2.12   0.035    -.1450289   -.0052653
            3  |  -.0382871   .0285706    -1.34   0.182     -.094714    .0181398
            4  |  -.0606977   .0253595    -2.39   0.018    -.1107826   -.0106128
            5  |  -.0367575   .0337915    -1.09   0.278    -.1034957    .0299806
            6  |   -.059384   .0299395    -1.98   0.049    -.1185144   -.0002536
            7  |   -.071021   .0375168    -1.89   0.060    -.1451165    .0030745
            9  |  -.2280365     .03878    -5.88   0.000     -.304627   -.1514461
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0156556   .0297902     0.53   0.600    -.0431799    .0744912
       foreign |  -.7405443   .0289534   -25.58   0.000    -.7977272   -.6833614
         _cons |   .4924362   .0960303     5.13   0.000     .3027767    .6820958
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local titles1 `"& All & "Low Propensity" & "Marginal Voters" & "High Propensity"\\ "'

. local titles2 "& & Bottom 25\% & 25-75\% & Top 25\% \\"

. local numbers "& (1) & (2) & (3) & (4) \\ \hline"

. local emptyrow "& & & &  \\ "

. local line "& & & & \hline \\ "

. 
. esttab using byvprop_voted21.tex, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01)
>  ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prehead(\begin{table}[htbp]\centering ///
> \def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi} ///
> \caption{Heterogeneity by Vote Propensity} ///
> \begin{tabular}{l*{4}{c}}\hline\hline) posthead("`header'" "`emptyrow'" `"`titles1'"' "`titles2'" "`numbers'" \\
>  \multicolumn{4}{c}{\textbf{Panel A: Voted in 2021}} \\ "`emptyrow'")  ///
> refcat(treated "", nolabel below) postfoot(\hline) fragment nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)
(output written to byvprop_voted21.tex)

. 
. 
. eststo clear

. 
. 
. eststo all: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal!=. &
>  voted21==0, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     27,678
                                                F(14, 243)        =      51.83
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0197
                                                Root MSE          =     .31942

                                (Std. err. adjusted for 244 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0059412   .0038924     1.53   0.128     -.001726    .0136083
     molincome |   .0094618   .0032354     2.92   0.004     .0030889    .0158348
        female |   .0553873    .006401     8.65   0.000     .0427788    .0679958
           age |   .0012278   .0007098     1.73   0.085    -.0001704     .002626
               |
       moses1d |
            1  |   .0914344   .0176785     5.17   0.000     .0566117    .1262571
            2  |   .0415274   .0121188     3.43   0.001     .0176562    .0653987
            3  |   .0431588    .010934     3.95   0.000     .0216214    .0646963
            4  |   .0199334   .0062364     3.20   0.002     .0076492    .0322177
            5  |   .0014651    .006367     0.23   0.818    -.0110766    .0140067
            6  |   .0455546   .0095319     4.78   0.000     .0267789    .0643303
            7  |   .0350395   .0099731     3.51   0.001     .0153948    .0546843
            9  |  -.0098591   .0123824    -0.80   0.427    -.0342496    .0145315
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0456637   .0043491    10.50   0.000     .0370969    .0542305
       foreign |  -.0519144   .0066224    -7.84   0.000    -.0649589   -.0388698
         _cons |  -.0624016   .0353221    -1.77   0.079    -.1319781     .007175
--------------------------------------------------------------------------------

. 
. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store all

. 
. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: all

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1 & voted2
> 1==0, cluster(kunta19)

. 
. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1 & voted21==
> 0, cluster(kunta19)

. 
. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo low: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1 & vo
> ted21==0, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      8,789
                                                F(14, 171)        =       3.89
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0045
                                                Root MSE          =     .24186

                                (Std. err. adjusted for 172 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0090073   .0047245     1.91   0.058    -.0003185    .0183332
     molincome |   .0031265   .0031558     0.99   0.323    -.0031029    .0093559
        female |   .0176979   .0082545     2.14   0.033      .001404    .0339918
           age |   .0002257   .0009096     0.25   0.804    -.0015698    .0020212
               |
       moses1d |
            1  |   .0636175   .0523659     1.21   0.226    -.0397494    .1669843
            2  |    .028009   .0194673     1.44   0.152    -.0104182    .0664362
            3  |    .049662    .024844     2.00   0.047     .0006216    .0987024
            4  |   .0228978   .0081941     2.79   0.006     .0067233    .0390724
            5  |  -.0015081   .0073323    -0.21   0.837    -.0159816    .0129654
            6  |   .0060047   .0122574     0.49   0.625    -.0181906    .0302001
            7  |   .0222277   .0266525     0.83   0.405    -.0303825    .0748379
            9  |    -.00361   .0103226    -0.35   0.727    -.0239861    .0167662
               |
     firstvote |          0  (omitted)
1.mohighschool |  -.0037017   .0167973    -0.22   0.826    -.0368584    .0294549
       foreign |  -.0156914   .0083568    -1.88   0.062    -.0321872    .0008044
         _cons |   .0116187   .0382208     0.30   0.762    -.0638266    .0870641
--------------------------------------------------------------------------------

. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store low

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: low

. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal==1 & voted2
> 1==0, cluster(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1 & voted21=
> =0, cluster(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo marginal: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if marginal
> ==1 & voted21==0, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =     14,177
                                                F(14, 213)        =       7.84
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0032
                                                Root MSE          =     .33027

                                (Std. err. adjusted for 214 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0116291   .0058452     1.99   0.048     .0001073     .023151
     molincome |    .012188   .0044448     2.74   0.007     .0034266    .0209494
        female |   .0228542   .0080614     2.84   0.005     .0069638    .0387445
           age |   .0008837   .0009721     0.91   0.364    -.0010324    .0027998
               |
       moses1d |
            1  |   .0646089   .0308305     2.10   0.037     .0038369     .125381
            2  |   .0440365   .0178853     2.46   0.015     .0087816    .0792914
            3  |   .0085901   .0153605     0.56   0.577     -.021688    .0388681
            4  |   .0127231   .0116706     1.09   0.277    -.0102815    .0357278
            5  |   .0132657   .0111676     1.19   0.236    -.0087476    .0352789
            6  |     .03456   .0169597     2.04   0.043     .0011298    .0679903
            7  |   .0181102   .0151994     1.19   0.235    -.0118504    .0480708
            9  |  -.0054253   .0187403    -0.29   0.772    -.0423655     .031515
               |
     firstvote |          0  (omitted)
1.mohighschool |   .0181301   .0057546     3.15   0.002     .0067868    .0294733
       foreign |  -.1107754   .0161102    -6.88   0.000    -.1425312   -.0790196
         _cons |  -.0548776   .0474381    -1.16   0.249    -.1483859    .0386307
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "Marginal"

added macro:
             e(label1) : "Marginal"

. estadd local label2 "- High"

added macro:
             e(label2) : "- High"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store marginal

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: marginal

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1 & voted21=
> =0, cluster(kunta19)

. matrix Brep1=e(b)

. matrix Vrep1=e(V)

. scalar df1=e(df_r)

. 
. 
. qui: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if never==1 & voted21==
> 0, cluster(kunta19)

. matrix Brep2=e(b)

. matrix Vrep2=e(V)

. scalar df2=e(df_r)

. 
. scalar df=df1+df2

. 
. scalar diffe=Brep1[1,1]-Brep2[1,1]

. scalar stde=sqrt(Vrep1[1,1]+Vrep2[1,1])

. 
. eststo high: reg voted22 treated molincome female age i.moses1d firstvote i.mohighschool foreign if always==1 & 
> voted21==0, cluster(kunta19)
note: firstvote omitted because of collinearity.

Linear regression                               Number of obs     =      4,712
                                                F(13, 156)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0110
                                                Root MSE          =     .39852

                                (Std. err. adjusted for 157 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |  -.0162772    .010047    -1.62   0.107     -.036123    .0035686
     molincome |   .0101408   .0071268     1.42   0.157    -.0039367    .0242182
        female |   .0795766   .0133683     5.95   0.000     .0531703    .1059829
           age |   .0030038   .0014526     2.07   0.040     .0001345     .005873
               |
       moses1d |
            1  |   .0350507   .0418347     0.84   0.403    -.0475848    .1176862
            2  |  -.0317849   .0461943    -0.69   0.492    -.1230319     .059462
            3  |    .024338   .0534053     0.46   0.649    -.0811528    .1298287
            4  |  -.0123709   .0461048    -0.27   0.789     -.103441    .0786993
            5  |    .005431   .0624962     0.09   0.931    -.1180169    .1288789
            6  |   .0633645   .0519167     1.22   0.224    -.0391859     .165915
            7  |   .0009076   .0652754     0.01   0.989      -.12803    .1298453
            9  |  -.0361808   .1094987    -0.33   0.742    -.2524721    .1801106
               |
     firstvote |          0  (omitted)
1.mohighschool |  -.0295001   .0281144    -1.05   0.296     -.085034    .0260338
       foreign |  -.2170276   .0454934    -4.77   0.000    -.3068903    -.127165
         _cons |   .0058075   .0820515     0.07   0.944    -.1562677    .1678828
--------------------------------------------------------------------------------

. 
. mystars diffe stde df

. estadd local label1 "High"

added macro:
             e(label1) : "High"

. estadd local label2 "- Low"

added macro:
             e(label2) : "- Low"

. estadd local controls "Yes"

added macro:
           e(controls) : "Yes"

. estimates store high

. qui: mean voted22 if treated==0 & e(sample)==1

. estadd scalar umean=r(table)[1,1]: high

. 
. 
. 
. 
. 
. 
. 
. 
. esttab, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("controls Controls " "umean Untreated $\bar{Y}$" "N Observations" "label1 \hline" "label2 \phantom{label
> 2}" "d_stars Differences" "se_stars \phantom{se}") ///
> sfmt(%9.3f %9.3f %9.0fc %9.3f) mlabels(none) nonumbers prefoot(\hline)  posthead("`emptyrow'" \multicolumn{4}{c}
> {\textbf{Panel B: Did Not Vote in 2021}} \\ "`emptyrow'")  ///
> refcat(treated "", nolabel below) postfoot(\hline\hline \\ \end{tabular} \\ \end{table}) fragment append nonotes
>  
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

& & & &  \\ 
\multicolumn{4}{c}{\textbf{Panel
B:
Did
Not
Vote
in
2021}}
\\
& & & &  \\ 
Treated                     0.006           0.009*          0.012**        -0.016   
                          (0.004)         (0.005)         (0.006)         (0.010)   

                                                                                    
\hline
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.115           0.057           0.118           0.210   
Observations               27,678           8,789          14,177           4,712   
\hline                                   Marginal        Marginal            High   
\phantom{label2}                            - Low          - High           - Low   
Differences                                 0.003         0.028**        -0.025**   
\phantom{se}                              (0.008)         (0.012)         (0.011)   
\hline\hline \\ \end{tabular} \\ \end{table}

. 
end of do-file

. do "$home\dofiles\TableA19.do"

. ****************************************************************************************************************
> ****
. *****Do-file for Tables A19
. *****Who is mobilized to vote by short text messages? Evidence from a nationwide field experiment with young vot
> ers
. ****************************************************************************************************************
> ****
. *****Last edited 24/6/5
. ****************************************************************************************************************
> ****
. *****Ado packages needed: estout
. ****************************************************************************************************************
> ****
. 
. 
. clear all

. 
. 
. *Use data
. use \data\dataforanalysis230522_v2.dta, clear

. *Set basecategory
. fvset base 8 moses1d

. label variable treated "Treated"

. 
. 
. 
. *************************************************
. *****Table A19
. *************************************************
. 
. *Generate dummies for mother's income group
. sum molincome if voted22!=.  & treated!=. & female!=., detail

                          molincome
-------------------------------------------------------------
      Percentiles      Smallest
 1%     7.244227        4.60517
 5%     8.682708        4.60517
10%     9.126959        4.60517       Obs              49,679
25%     9.809176        4.60517       Sum of wgt.      49,679

50%      10.2989                      Mean            10.1012
                        Largest       Std. dev.      .7909808
75%     10.56101       11.87687
90%     10.82576       11.87687       Variance       .6256507
95%     10.99876       11.87687       Skewness      -1.824325
99%     11.43388       11.87687       Kurtosis       8.889554

. gen lowincome=molincome<r(p50) & molincome!=.

. gen highincome=molincome>=r(p50) & molincome!=.

. 
. 
. *Generate dummies for mother's profession group
. replace ammattikoodi_k="." if ammattikoodi_k=="XXX"
(52,589 real changes made)

. gen prof1d=real(substr(ammattikoodi_k,1,1)) 
(2,086,799 missing values generated)

. 
. gen mmprof1d=prof1d if age==mage & female==1
(3,070,195 missing values generated)

. bys petu: egen mprof1d=mean(mmprof1d)
(2,016,212 missing values generated)

. replace mprof1d=. if a25lkm==0 | a25lkm==. | diffage<16 | age>25
(966,416 real changes made, 966,416 to missing)

. 
. gen moprof1d=mprof1d
(2,982,628 missing values generated)

. replace moprof1d=prof1d if moprof1d==.
(1,369,837 real changes made)

. 
. 
. gen lowskill=.
(3,574,284 missing values generated)

. replace lowskill=1 if moprof1d>3 & moprof1d!=.
(1,025,776 real changes made)

. replace lowskill=1 if moprof1d==0
(5,020 real changes made)

. gen highskill=.
(3,574,284 missing values generated)

. replace  highskill=1 if moprof1d<4 & moprof1d>0
(930,697 real changes made)

. 
. 
. 
. eststo clear

. eststo M1: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if lowincome==1, 
> cluster(kunta19)

Linear regression                               Number of obs     =     24,805
                                                F(15, 276)        =     157.69
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0708
                                                Root MSE          =     .44049

                                (Std. err. adjusted for 277 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0139568   .0050234     2.78   0.006     .0040678    .0238458
     molincome |   .0021667   .0058566     0.37   0.712    -.0093625     .013696
        female |   .0906901   .0074347    12.20   0.000     .0760541     .105326
           age |   .0076933    .000978     7.87   0.000      .005768    .0096186
       foreign |  -.1426003   .0102943   -13.85   0.000    -.1628657   -.1223349
               |
       moses1d |
            1  |   .1935171   .0246731     7.84   0.000     .1449458    .2420884
            2  |   .0671244   .0161592     4.15   0.000     .0353134    .0989354
            3  |   .1713443   .0212802     8.05   0.000     .1294523    .2132364
            4  |   .0525737    .009143     5.75   0.000     .0345749    .0705725
            5  |   .0103509   .0104738     0.99   0.324    -.0102677    .0309695
            6  |   .0728052   .0134714     5.40   0.000     .0462855    .0993248
            7  |   .0661827   .0139885     4.73   0.000      .038645    .0937203
            9  |   .0102837   .0160004     0.64   0.521    -.0212147    .0417822
               |
     firstvote |   .1058301   .0159399     6.64   0.000     .0744509    .1372094
1.mohighschool |   .1498271   .0096944    15.46   0.000     .1307427    .1689114
         _cons |  -.0404975   .0522445    -0.78   0.439    -.1433459    .0623508
--------------------------------------------------------------------------------

. estadd local Controls "Yes"

added macro:
           e(Controls) : "Yes"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                          Number of obs = 9,903

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .2893063   .0045568       .280374    .2982385
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M1

. eststo M2: reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if highincome==1,
>  cluster(kunta19)

Linear regression                               Number of obs     =     24,874
                                                F(15, 276)        =      86.09
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0548
                                                Root MSE          =      .4575

                                (Std. err. adjusted for 277 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0042101   .0044904     0.94   0.349    -.0046298    .0130499
     molincome |   .0543144   .0112923     4.81   0.000     .0320845    .0765444
        female |   .1185052   .0090498    13.09   0.000     .1006897    .1363206
           age |   .0061437   .0016933     3.63   0.000     .0028102    .0094771
       foreign |  -.1343602   .0195189    -6.88   0.000     -.172785   -.0959354
               |
       moses1d |
            1  |   .1420791   .0422897     3.36   0.001     .0588276    .2253305
            2  |   .0394112   .0220487     1.79   0.075    -.0039938    .0828162
            3  |   .0775775   .0188619     4.11   0.000      .040446     .114709
            4  |   .0104069   .0199542     0.52   0.602    -.0288749    .0496886
            5  |  -.0267939   .0224715    -1.19   0.234    -.0710313    .0174434
            6  |   .0191303   .0481493     0.40   0.691    -.0756561    .1139168
            7  |   .1058651   .0369405     2.87   0.004     .0331442    .1785861
            9  |  -.0291302   .0304221    -0.96   0.339     -.089019    .0307585
               |
     firstvote |    .131077   .0135952     9.64   0.000     .1043134    .1578405
1.mohighschool |   .1098742   .0079822    13.76   0.000     .0941605    .1255879
         _cons |  -.5113409   .1314228    -3.89   0.000    -.7700593   -.2526224
--------------------------------------------------------------------------------

. estadd local Controls "Yes"

added macro:
           e(Controls) : "Yes"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                          Number of obs = 9,976

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3272855   .0046981      .3180763    .3364947
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M2

. eststo M3:  reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if lowskill==1, 
> cluster(kunta19)

Linear regression                               Number of obs     =     24,726
                                                F(15, 282)        =      90.28
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0422
                                                Root MSE          =     .43183

                                (Std. err. adjusted for 283 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0121116   .0058785     2.06   0.040     .0005403    .0236828
     molincome |  -.0129105   .0069755    -1.85   0.065    -.0266412    .0008202
        female |   .0881863   .0083805    10.52   0.000       .07169    .1046825
           age |   .0063719   .0012573     5.07   0.000     .0038971    .0088467
       foreign |  -.1364147   .0092949   -14.68   0.000    -.1547109   -.1181186
               |
       moses1d |
            1  |   .1955497   .0238578     8.20   0.000     .1485878    .2425116
            2  |   .0363141   .0191626     1.90   0.059    -.0014058     .074034
            3  |   .1590828   .0702824     2.26   0.024     .0207381    .2974275
            4  |   .0430532   .0155948     2.76   0.006     .0123562    .0737501
            5  |   .0133441   .0133441     1.00   0.318    -.0129226    .0396108
            6  |   .1252994   .0391947     3.20   0.002      .048148    .2024508
            7  |   .0620657   .0203026     3.06   0.002     .0221019    .1020295
            9  |  -.0637166   .0373678    -1.71   0.089    -.1372718    .0098387
               |
     firstvote |   .1040449   .0171609     6.06   0.000     .0702651    .1378247
1.mohighschool |   .1137709   .0093892    12.12   0.000     .0952891    .1322526
         _cons |   .1470012   .0693032     2.12   0.035     .0105839    .2834184
--------------------------------------------------------------------------------

. estadd local Controls "Yes"

added macro:
           e(Controls) : "Yes"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                          Number of obs = 9,829

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .2571981    .004409      .2485556    .2658406
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M3

. eststo M4:  reg voted22 treated molincome female age foreign i.moses1d firstvote i.mohighschool if highskill==1,
>  cluster(kunta19)

Linear regression                               Number of obs     =     17,001
                                                F(14, 253)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0422
                                                Root MSE          =     .47946

                                (Std. err. adjusted for 254 clusters in kunta19)
--------------------------------------------------------------------------------
               |               Robust
       voted22 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
       treated |   .0040982   .0065005     0.63   0.529    -.0087038    .0169002
     molincome |  -.0042352   .0049952    -0.85   0.397    -.0140727    .0056022
        female |   .1295437   .0062667    20.67   0.000      .117202    .1418853
           age |   .0091381   .0020454     4.47   0.000     .0051098    .0131664
       foreign |  -.1678857   .0270209    -6.21   0.000    -.2211003   -.1146711
               |
       moses1d |
            1  |   .6250276    .053121    11.77   0.000      .520412    .7296432
            2  |   .0317059   .0516576     0.61   0.540    -.0700278    .1334396
            3  |   .0403993   .0496432     0.81   0.417    -.0573672    .1381658
            4  |  -.0242634   .0476476    -0.51   0.611    -.1180999    .0695731
            5  |    .074886    .122323     0.61   0.541    -.1660151    .3157871
            6  |   .0458671   .1166352     0.39   0.694    -.1838325    .2755667
            7  |   .1167053   .0815798     1.43   0.154    -.0439567    .2773672
            9  |  -.0387332   .0957498    -0.40   0.686    -.2273014    .1498349
               |
     firstvote |   .1495341   .0173763     8.61   0.000     .1153135    .1837547
1.mohighschool |   .1049541   .0101009    10.39   0.000     .0850616    .1248466
         _cons |   .1047556   .0887896     1.18   0.239    -.0701053    .2796166
--------------------------------------------------------------------------------

. estadd local Controls "Yes"

added macro:
           e(Controls) : "Yes"

. mean voted22 if e(sample)==1 & treated==0

Mean estimation                          Number of obs = 6,880

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
     voted22 |   .3956395   .0058957      .3840821    .4071969
--------------------------------------------------------------

. estadd scalar umean=r(table)[1,1]: M4

. local header "& \multicolumn{4}{c}{Outcome: Voted} \\"

. local numbers "& (1) & (2) & (3) & (4)\\ \hline"

. local titles1 "& < Median Income & > Median Income  & Low Skilled Occupation & High Skilled Occupation"

. local emptyrow "& & & &   \\ "

. local line "& & & &  \hline \\ "

. 
. local dstars " "

. local sestars " "

. esttab, keep (treated) noobs label se b(%9.3f) se(%9.3f) star(* 0.10 ** 0.05 *** 0.01) ///
> scalars("Controls Controls" "umean Untreated $\bar{Y}$" "N Observations") ///
> sfmt(%9.3f %9.3f %9.0fc) mlabels(none) nonumbers posthead("`header'" "`emptyrow'" "`numbers'" "`emptyrow'") ///
> refcat(treatedf "", nolabel below) title("Heterogeneity by Income and Profession Skill Level") nonotes replace
(tabulating estimates stored by eststo; specify "." to tabulate the active results)

Heterogeneity by Income and Profession Skill Level
------------------------------------------------------------------------------------
& \multicolumn{4}{c}{Outcome: Voted} \\
& & & &   \\ 
& (1) & (2) & (3) & (4)\\ \hline
& & & &   \\ 
Treated                     0.014***        0.004           0.012**         0.004   
                          (0.005)         (0.004)         (0.006)         (0.007)   
------------------------------------------------------------------------------------
Controls                      Yes             Yes             Yes             Yes   
Untreated $\bar{Y}$         0.289           0.327           0.257           0.396   
Observations               24,805          24,874          24,726          17,001   
------------------------------------------------------------------------------------

. 
end of do-file

. do "$home\dofiles\FigureA1.do"

. ****************************************************************************************************************
> ****
. *****Do-file for Figure A1
. *****Who is mobilized to vote by short text messages? Evidence from a nationwide field experiment with young vot
> ers
. ****************************************************************************************************************
> ****
. *****Last edited 24/6/5
. ****************************************************************************************************************
> ****
. *****Ado packages needed: estout
. ****************************************************************************************************************
> ****
. 
. 
. *set seed
. set seed 23581017

. *Use data
. use \data\dataforanalysis230522_v2.dta, clear

. 
. *************************************************
. *****Figure A1
. *************************************************
. 
. 
. *Split sample and generate predictions
. keep if treatment==0
(3,553,844 observations deleted)

. 
. splitsample, gen(ssample1) values(0 1)

. 
. 
. logit voted22 female molincome  firstvote foreign i.moses1d i.mohighschool i.kunta19 if ssample1==0

note: 20.kunta19 != 0 predicts failure perfectly;
      20.kunta19 omitted and 3 obs not used.

note: 46.kunta19 != 0 predicts failure perfectly;
      46.kunta19 omitted and 1 obs not used.

note: 72.kunta19 != 0 predicts failure perfectly;
      72.kunta19 omitted and 2 obs not used.

note: 74.kunta19 != 0 predicts failure perfectly;
      74.kunta19 omitted and 5 obs not used.

note: 75.kunta19 != 0 predicts success perfectly;
      75.kunta19 omitted and 2 obs not used.

note: 78.kunta19 != 0 predicts failure perfectly;
      78.kunta19 omitted and 1 obs not used.

note: 90.kunta19 != 0 predicts failure perfectly;
      90.kunta19 omitted and 1 obs not used.

note: 98.kunta19 != 0 predicts failure perfectly;
      98.kunta19 omitted and 2 obs not used.

note: 102.kunta19 != 0 predicts failure perfectly;
      102.kunta19 omitted and 1 obs not used.

note: 105.kunta19 != 0 predicts failure perfectly;
      105.kunta19 omitted and 1 obs not used.

note: 111.kunta19 != 0 predicts failure perfectly;
      111.kunta19 omitted and 3 obs not used.

note: 148.kunta19 != 0 predicts failure perfectly;
      148.kunta19 omitted and 2 obs not used.

note: 172.kunta19 != 0 predicts failure perfectly;
      172.kunta19 omitted and 1 obs not used.

note: 177.kunta19 != 0 predicts failure perfectly;
      177.kunta19 omitted and 8 obs not used.

note: 178.kunta19 != 0 predicts success perfectly;
      178.kunta19 omitted and 1 obs not used.

note: 218.kunta19 != 0 predicts failure perfectly;
      218.kunta19 omitted and 1 obs not used.

note: 231.kunta19 != 0 predicts failure perfectly;
      231.kunta19 omitted and 3 obs not used.

note: 232.kunta19 != 0 predicts failure perfectly;
      232.kunta19 omitted and 6 obs not used.

note: 241.kunta19 != 0 predicts failure perfectly;
      241.kunta19 omitted and 3 obs not used.

note: 250.kunta19 != 0 predicts failure perfectly;
      250.kunta19 omitted and 1 obs not used.

note: 260.kunta19 != 0 predicts failure perfectly;
      260.kunta19 omitted and 3 obs not used.

note: 273.kunta19 != 0 predicts failure perfectly;
      273.kunta19 omitted and 1 obs not used.

note: 280.kunta19 != 0 predicts success perfectly;
      280.kunta19 omitted and 1 obs not used.

note: 288.kunta19 != 0 predicts success perfectly;
      288.kunta19 omitted and 1 obs not used.

note: 291.kunta19 != 0 predicts success perfectly;
      291.kunta19 omitted and 1 obs not used.

note: 301.kunta19 != 0 predicts failure perfectly;
      301.kunta19 omitted and 1 obs not used.

note: 312.kunta19 != 0 predicts failure perfectly;
      312.kunta19 omitted and 1 obs not used.

note: 317.kunta19 != 0 predicts failure perfectly;
      317.kunta19 omitted and 1 obs not used.

note: 320.kunta19 != 0 predicts failure perfectly;
      320.kunta19 omitted and 1 obs not used.

note: 399.kunta19 != 0 predicts failure perfectly;
      399.kunta19 omitted and 2 obs not used.

note: 407.kunta19 != 0 predicts failure perfectly;
      407.kunta19 omitted and 1 obs not used.

note: 433.kunta19 != 0 predicts failure perfectly;
      433.kunta19 omitted and 4 obs not used.

note: 434.kunta19 != 0 predicts failure perfectly;
      434.kunta19 omitted and 2 obs not used.

note: 483.kunta19 != 0 predicts success perfectly;
      483.kunta19 omitted and 1 obs not used.

note: 484.kunta19 != 0 predicts failure perfectly;
      484.kunta19 omitted and 3 obs not used.

note: 541.kunta19 != 0 predicts failure perfectly;
      541.kunta19 omitted and 2 obs not used.

note: 560.kunta19 != 0 predicts failure perfectly;
      560.kunta19 omitted and 2 obs not used.

note: 595.kunta19 != 0 predicts failure perfectly;
      595.kunta19 omitted and 2 obs not used.

note: 599.kunta19 != 0 predicts failure perfectly;
      599.kunta19 omitted and 3 obs not used.

note: 601.kunta19 != 0 predicts success perfectly;
      601.kunta19 omitted and 1 obs not used.

note: 620.kunta19 != 0 predicts failure perfectly;
      620.kunta19 omitted and 6 obs not used.

note: 631.kunta19 != 0 predicts failure perfectly;
      631.kunta19 omitted and 1 obs not used.

note: 636.kunta19 != 0 predicts failure perfectly;
      636.kunta19 omitted and 1 obs not used.

note: 686.kunta19 != 0 predicts success perfectly;
      686.kunta19 omitted and 2 obs not used.

note: 689.kunta19 != 0 predicts failure perfectly;
      689.kunta19 omitted and 1 obs not used.

note: 694.kunta19 != 0 predicts failure perfectly;
      694.kunta19 omitted and 8 obs not used.

note: 702.kunta19 != 0 predicts failure perfectly;
      702.kunta19 omitted and 1 obs not used.

note: 704.kunta19 != 0 predicts success perfectly;
      704.kunta19 omitted and 1 obs not used.

note: 707.kunta19 != 0 predicts success perfectly;
      707.kunta19 omitted and 1 obs not used.

note: 748.kunta19 != 0 predicts success perfectly;
      748.kunta19 omitted and 1 obs not used.

note: 755.kunta19 != 0 predicts failure perfectly;
      755.kunta19 omitted and 2 obs not used.

note: 762.kunta19 != 0 predicts failure perfectly;
      762.kunta19 omitted and 5 obs not used.

note: 777.kunta19 != 0 predicts success perfectly;
      777.kunta19 omitted and 1 obs not used.

note: 778.kunta19 != 0 predicts failure perfectly;
      778.kunta19 omitted and 1 obs not used.

note: 783.kunta19 != 0 predicts failure perfectly;
      783.kunta19 omitted and 1 obs not used.

note: 832.kunta19 != 0 predicts failure perfectly;
      832.kunta19 omitted and 2 obs not used.

note: 845.kunta19 != 0 predicts failure perfectly;
      845.kunta19 omitted and 10 obs not used.

note: 846.kunta19 != 0 predicts failure perfectly;
      846.kunta19 omitted and 1 obs not used.

note: 854.kunta19 != 0 predicts failure perfectly;
      854.kunta19 omitted and 1 obs not used.

note: 857.kunta19 != 0 predicts failure perfectly;
      857.kunta19 omitted and 1 obs not used.

note: 858.kunta19 != 0 predicts failure perfectly;
      858.kunta19 omitted and 8 obs not used.

note: 886.kunta19 != 0 predicts failure perfectly;
      886.kunta19 omitted and 7 obs not used.

note: 893.kunta19 != 0 predicts failure perfectly;
      893.kunta19 omitted and 2 obs not used.

note: 922.kunta19 != 0 predicts failure perfectly;
      922.kunta19 omitted and 1 obs not used.

note: 936.kunta19 != 0 predicts failure perfectly;
      936.kunta19 omitted and 1 obs not used.

note: 976.kunta19 != 0 predicts failure perfectly;
      976.kunta19 omitted and 1 obs not used.

Iteration 0:   log likelihood = -6048.6746  
Iteration 1:   log likelihood = -5609.1828  
Iteration 2:   log likelihood =  -5597.671  
Iteration 3:   log likelihood = -5597.6113  
Iteration 4:   log likelihood = -5597.6112  

Logistic regression                                     Number of obs =  9,776
                                                        LR chi2(189)  = 902.13
                                                        Prob > chi2   = 0.0000
Log likelihood = -5597.6112                             Pseudo R2     = 0.0746

--------------------------------------------------------------------------------
       voted22 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
        female |   .5499551   .0468655    11.73   0.000     .4581005    .6418097
     molincome |   .0488045   .0367116     1.33   0.184     -.023149     .120758
     firstvote |   .4357414    .118129     3.69   0.000     .2042127      .66727
       foreign |  -.6905409    .159861    -4.32   0.000    -1.003863    -.377219
               |
       moses1d |
            2  |  -.4521563   .2052948    -2.20   0.028    -.8545266   -.0497859
            3  |  -.2719594   .1925629    -1.41   0.158    -.6493757     .105457
            4  |  -.5433164   .1856946    -2.93   0.003     -.907271   -.1793617
            5  |  -.7814817   .1912637    -4.09   0.000    -1.156352   -.4066118
            6  |  -.4041159   .2077303    -1.95   0.052    -.8112597     .003028
            7  |  -.3816513   .2177189    -1.75   0.080    -.8083725      .04507
            8  |  -.7302079   .2086383    -3.50   0.000    -1.139131   -.3212844
            9  |   -.816932   .2496638    -3.27   0.001    -1.306264      -.3276
               |
1.mohighschool |   .6851269   .0505062    13.57   0.000     .5861366    .7841172
               |
       kunta19 |
            9  |   1.524975   .5539837     2.75   0.006     .4391865    2.610763
           10  |  -.2247794   .4033849    -0.56   0.577    -1.015399    .5658404
           19  |  -.2594869   .6756616    -0.38   0.701    -1.583759    1.064786
           20  |          0  (empty)
           46  |          0  (empty)
           49  |  -.0391276   .3889146    -0.10   0.920    -.8013863     .723131
           50  |   .9649941   1.056348     0.91   0.361     -1.10541    3.035398
           51  |  -.2028066   .4991933    -0.41   0.685    -1.181207    .7755943
           61  |   .6940179   .8759718     0.79   0.428    -1.022855    2.410891
           69  |  -.0711519    .445004    -0.16   0.873    -.9433438      .80104
           71  |  -.1355692   .4547501    -0.30   0.766    -1.026863    .7557248
           72  |          0  (empty)
           74  |          0  (empty)
           75  |          0  (empty)
           77  |  -.1526761   .6885845    -0.22   0.825    -1.502277    1.196925
           78  |          0  (empty)
           79  |  -1.294502    .808167    -1.60   0.109     -2.87848    .2894765
           86  |  -.0253027   .5912596    -0.04   0.966     -1.18415    1.133545
           90  |          0  (empty)
           91  |   .0035514   .3419234     0.01   0.992    -.6666063     .673709
           92  |  -.4349338   .3069659    -1.42   0.157    -1.036576    .1667083
           98  |          0  (empty)
           99  |   .4590919   1.513031     0.30   0.762    -2.506394    3.424578
          102  |          0  (empty)
          105  |          0  (empty)
          106  |  -.5224882   .6412269    -0.81   0.415     -1.77927    .7342935
          108  |  -.0755782   .4742798    -0.16   0.873     -1.00515    .8539931
          109  |  -.1787661   .5146602    -0.35   0.728    -1.187482    .8299494
          111  |          0  (empty)
          139  |    .980677   1.064472     0.92   0.357    -1.105649    3.067003
          140  |  -.9266308   .8566849    -1.08   0.279    -2.605702    .7524407
          143  |   .9917974   .7880774     1.26   0.208     -.552806    2.536401
          145  |  -.2385796   1.227856    -0.19   0.846    -2.645133    2.167974
          146  |  -.0491443   .6674894    -0.07   0.941    -1.357399    1.259111
          148  |          0  (empty)
          151  |   .4119299   .7162797     0.58   0.565    -.9919525    1.815812
          153  |   .5256498   1.448396     0.36   0.717    -2.313155    3.364454
          165  |  -.5834412   .4812803    -1.21   0.225    -1.526733     .359851
          167  |  -.1938382   .3116526    -0.62   0.534    -.8046662    .4169897
          169  |    .364875   .5571097     0.65   0.513      -.72704     1.45679
          171  |   .9853264   1.446257     0.68   0.496    -1.849285    3.819938
          172  |          0  (empty)
          176  |   .1595851   1.348913     0.12   0.906    -2.484237    2.803407
          177  |          0  (empty)
          178  |          0  (empty)
          179  |    .047209   .3033078     0.16   0.876    -.5472634    .6416814
          182  |   .1190874   .3913151     0.30   0.761    -.6478762    .8860509
          186  |    -.72962    .842544    -0.87   0.387    -2.380976    .9217359
          202  |   -.123996   .3832275    -0.32   0.746     -.875108    .6271161
          205  |  -.5402412   .6373401    -0.85   0.397    -1.789405    .7089225
          208  |  -.2503579   .8962504    -0.28   0.780    -2.006976     1.50626
          211  |  -.5241613    .650882    -0.81   0.421    -1.799867    .7515441
          214  |  -.4582071    1.23004    -0.37   0.710    -2.869041    1.952627
          216  |  -.4384552   1.167246    -0.38   0.707    -2.726215    1.849304
          217  |   .0501809   .5439482     0.09   0.926    -1.015938      1.1163
          218  |          0  (empty)
          224  |  -.0092106   .5043163    -0.02   0.985    -.9976525    .9792313
          226  |  -.1281205   .6797578    -0.19   0.851    -1.460421     1.20418
          230  |   .9800351    .881834     1.11   0.266    -.7483278    2.708398
          231  |          0  (empty)
          232  |          0  (empty)
          233  |   -.697412   1.157691    -0.60   0.547    -2.966445    1.571621
          236  |   .4741985   .5262815     0.90   0.368    -.5572943    1.505691
          239  |  -.3471475    .860498    -0.40   0.687    -2.033693    1.339398
          240  |  -.0594537   .4258818    -0.14   0.889    -.8941666    .7752593
          241  |          0  (empty)
          244  |  -.1226357   .3996368    -0.31   0.759    -.9059095    .6606381
          245  |  -.5072392   .3628561    -1.40   0.162    -1.218424    .2039457
          249  |   2.180575   1.323428     1.65   0.099    -.4132963    4.774446
          250  |          0  (empty)
          257  |  -.3120588   .3786496    -0.82   0.410    -1.054198    .4300809
          260  |          0  (empty)
          261  |   .2684717   .9025544     0.30   0.766    -1.500502    2.037446
          263  |  -.4802889   1.210973    -0.40   0.692    -2.853752    1.893174
          265  |   .1935492   1.294842     0.15   0.881    -2.344295    2.731393
          271  |   .1126359   1.459463     0.08   0.938    -2.747858     2.97313
          272  |   .2737058   .5137582     0.53   0.594    -.7332417    1.280653
          273  |          0  (empty)
          276  |   .1773621   .4068039     0.44   0.663    -.6199588    .9746831
          280  |          0  (empty)
          285  |  -1.814792   1.095489    -1.66   0.098    -3.961911    .3323273
          286  |  -.6284032   .7228819    -0.87   0.385    -2.045226    .7884193
          287  |    .351698   .4786699     0.73   0.462    -.5864778    1.289874
          288  |          0  (empty)
          291  |          0  (empty)
          297  |   .4774722   .4155933     1.15   0.251    -.3370757     1.29202
          300  |  -.0186396   .6474756    -0.03   0.977    -1.287669    1.250389
          301  |          0  (empty)
          305  |  -.5897383   .4644789    -1.27   0.204      -1.5001    .3206237
          309  |  -1.343788   .8017588    -1.68   0.094    -2.915206    .2276304
          312  |          0  (empty)
          317  |          0  (empty)
          320  |          0  (empty)
          322  |  -.1405585   .5609282    -0.25   0.802    -1.239958    .9588405
          398  |  -.2616194   .5145891    -0.51   0.611    -1.270196    .7469568
          399  |          0  (empty)
          400  |  -.5124892   1.166135    -0.44   0.660    -2.798071    1.773093
          402  |  -.3594823   .4544932    -0.79   0.429    -1.250273    .5313079
          403  |   .2288604   .7847583     0.29   0.771    -1.309238    1.766958
          405  |   .5550455   .4649997     1.19   0.233    -.3563372    1.466428
          407  |          0  (empty)
          408  |   -.151227    .948094    -0.16   0.873    -2.009457    1.707003
          410  |  -.1872479   .4178494    -0.45   0.654    -1.006218    .6317218
          418  |   .6393201   .6069935     1.05   0.292    -.5503653    1.829005
          420  |   1.309602   1.458571     0.90   0.369    -1.549145    4.168349
          422  |  -.4498784   .5067033    -0.89   0.375    -1.442999    .5432417
          423  |   .0033216   1.288892     0.00   0.998    -2.522861    2.529504
          425  |   .7601192    .433675     1.75   0.080    -.0898682    1.610107
          426  |  -1.057896   .5064438    -2.09   0.037    -2.050507    -.065284
          430  |   1.061157   1.066818     0.99   0.320    -1.029769    3.152082
          433  |          0  (empty)
          434  |          0  (empty)
          436  |   .1202201   .7742593     0.16   0.877      -1.3973    1.637741
          441  |    .734382   .7003307     1.05   0.294    -.6382409    2.107005
          444  |  -.2151959    .342333    -0.63   0.530    -.8861562    .4557644
          445  |   .5473589   .4415017     1.24   0.215    -.3179685    1.412686
          475  |   .5545276    .501448     1.11   0.269    -.4282925    1.537348
          481  |  -.0702486    .576825    -0.12   0.903    -1.200805    1.060308
          483  |          0  (empty)
          484  |          0  (empty)
          491  |   -.248981   .6165775    -0.40   0.686    -1.457451    .9594886
          494  |   .5795558   .4441954     1.30   0.192    -.2910513    1.450163
          499  |    .207467   .7339099     0.28   0.777     -1.23097    1.645904
          500  |    .405396   .4952329     0.82   0.413    -.5652426    1.376035
          503  |  -.4937445   .5537287    -0.89   0.373    -1.579033    .5915437
          505  |   .3177494   1.020227     0.31   0.755    -1.681859    2.317358
          507  |  -1.314584    .815584    -1.61   0.107    -2.913099    .2839312
          508  |    .019453   .8038464     0.02   0.981    -1.556057    1.594963
          529  |  -.2586928   .9118612    -0.28   0.777    -2.045908    1.528522
          531  |  -.9024653   .7133039    -1.27   0.206    -2.300515    .4955847
          535  |  -.1329292   .4082466    -0.33   0.745    -.9330778    .6672193
          536  |  -.3261895   .7215203    -0.45   0.651    -1.740343    1.087964
          541  |          0  (empty)
          543  |  -.2721694   .3494664    -0.78   0.436     -.957111    .4127722
          545  |   .4954731   .8528269     0.58   0.561    -1.176037    2.166983
          560  |          0  (empty)
          562  |  -.4700803   1.155495    -0.41   0.684    -2.734808    1.794648
          563  |   .5220743   .8908416     0.59   0.558    -1.223943    2.268092
          564  |   .1588303   .3004296     0.53   0.597    -.4300009    .7476615
          577  |  -.7984996   .4978665    -1.60   0.109      -1.7743    .1773009
          578  |    1.25474   .6315608     1.99   0.047     .0169035    2.492576
          581  |  -.0527342   .5371604    -0.10   0.922    -1.105549    1.000081
          583  |   .7382668   1.515015     0.49   0.626    -2.231109    3.707642
          584  |   .9442739   .5732896     1.65   0.100    -.1793531    2.067901
          588  |   .3606933   1.333235     0.27   0.787    -2.252399    2.973785
          592  |  -1.063251   1.117466    -0.95   0.341    -3.253445    1.126943
          593  |  -1.618473   .6013936    -2.69   0.007    -2.797183   -.4397631
          595  |          0  (empty)
          598  |   .2739231   .8115994     0.34   0.736    -1.316783    1.864629
          599  |          0  (empty)
          601  |          0  (empty)
          604  |   -.934099   .7155043    -1.31   0.192    -2.336462    .4682637
          607  |   -.159359   .7478655    -0.21   0.831    -1.625148     1.30643
          608  |  -.2023315   .8958856    -0.23   0.821    -1.958235    1.553572
          609  |  -.0545516   .3217493    -0.17   0.865    -.6851687    .5760655
          611  |   .4792209   1.272015     0.38   0.706    -2.013883    2.972325
          614  |  -.0442809   .8843936    -0.05   0.960    -1.777661    1.689099
          615  |    .859375   1.067376     0.81   0.421    -1.232644    2.951394
          620  |          0  (empty)
          623  |  -.0875714   .9293042    -0.09   0.925    -1.908974    1.733831
          624  |   .0223906   .6317018     0.04   0.972    -1.215722    1.260503
          625  |   .3954763   1.274609     0.31   0.756    -2.102712    2.893665
          626  |  -.4888434   .6469806    -0.76   0.450    -1.756902    .7792153
          630  |   1.452979    1.44922     1.00   0.316    -1.387439    4.293398
          631  |          0  (empty)
          635  |   .4236087   .5930134     0.71   0.475    -.7386761    1.585894
          636  |          0  (empty)
          638  |  -.5632589     .37434    -1.50   0.132    -1.296952     .170434
          678  |   .0852853   .7731111     0.11   0.912    -1.429985    1.600555
          680  |  -.6173228   .4331083    -1.43   0.154    -1.466199    .2315538
          683  |   1.560329   .5631006     2.77   0.006     .4566717    2.663985
          684  |  -.6095003   .7230088    -0.84   0.399    -2.026572     .807571
          686  |          0  (empty)
          687  |   .8257912   .9704224     0.85   0.395    -1.076202    2.727784
          689  |          0  (empty)
          691  |   .2930134   .6505875     0.45   0.652    -.9821147    1.568142
          694  |          0  (empty)
          697  |   1.128412    1.47921     0.76   0.446    -1.770786     4.02761
          698  |   .0289326   .3188611     0.09   0.928    -.5960237    .6538889
          702  |          0  (empty)
          704  |          0  (empty)
          707  |          0  (empty)
          710  |  -.3729966   1.185243    -0.31   0.753     -2.69603    1.950037
          729  |  -.7382909   .6270427    -1.18   0.239    -1.967272    .4906903
          732  |   1.284658   1.088635     1.18   0.238    -.8490277    3.418344
          734  |   -.226295   .3491286    -0.65   0.517    -.9105746    .4579845
          740  |   .0096935   .6306318     0.02   0.988    -1.226322    1.245709
          743  |   .6297329   .4387544     1.44   0.151    -.2302099    1.489676
          746  |  -.0653623   .5010565    -0.13   0.896    -1.047415    .9166905
          747  |   -.963861   1.142516    -0.84   0.399    -3.203152     1.27543
          748  |          0  (empty)
          749  |   .2378662   1.449539     0.16   0.870    -2.603177     3.07891
          753  |  -.3036887   .4138124    -0.73   0.463    -1.114746    .5073687
          755  |          0  (empty)
          758  |   .3181146   .9475961     0.34   0.737     -1.53914    2.175369
          759  |  -.6628708   .8759377    -0.76   0.449    -2.379677    1.053936
          761  |  -1.435583   .6854567    -2.09   0.036    -2.779054    -.092113
          762  |          0  (empty)
          765  |  -.4207374   .5025656    -0.84   0.402    -1.405748     .564273
          768  |   .0860482   .7762042     0.11   0.912    -1.435284    1.607381
          777  |          0  (empty)
          778  |          0  (empty)
          781  |  -.0994468   .8876753    -0.11   0.911    -1.839259    1.640365
          783  |          0  (empty)
          785  |  -.3272818   1.178572    -0.28   0.781    -2.637241    1.982677
          790  |  -.2022612    .884336    -0.23   0.819    -1.935528    1.531005
          791  |  -.4175839   1.174152    -0.36   0.722    -2.718879    1.883711
          832  |          0  (empty)
          837  |   .3043451   .2993011     1.02   0.309    -.2822744    .8909646
          845  |          0  (empty)
          846  |          0  (empty)
          848  |  -.0271143   1.266828    -0.02   0.983    -2.510051    2.455822
          849  |   .7408503   .6214061     1.19   0.233    -.4770833    1.958784
          850  |    .825765   1.057582     0.78   0.435    -1.247058    2.898588
          851  |    .687516   .7543365     0.91   0.362    -.7909565    2.165988
          853  |   .5188085   .3567409     1.45   0.146    -.1803908    1.218008
          854  |          0  (empty)
          857  |          0  (empty)
          858  |          0  (empty)
          859  |   .9200913   .8312843     1.11   0.268    -.7091959    2.549378
          886  |          0  (empty)
          889  |   .5246714   .6256185     0.84   0.402    -.7015184    1.750861
          893  |          0  (empty)
          895  |  -.0922542   .4345986    -0.21   0.832    -.9440518    .7595434
          905  |  -.0162664    .318019    -0.05   0.959    -.6395721    .6070394
          908  |  -.4666844   .4064249    -1.15   0.251    -1.263262    .3298937
          915  |   .7097168   1.446046     0.49   0.624    -2.124482    3.543916
          922  |          0  (empty)
          924  |   1.023471   1.452049     0.70   0.481    -1.822493    3.869436
          925  |  -.9941607   .8236124    -1.21   0.227    -2.608411      .62009
          927  |  -.5503222   .3855458    -1.43   0.153    -1.305978    .2053336
          931  |  -.6523826   .7063097    -0.92   0.356    -2.036724     .731959
          934  |  -.0760792   1.304123    -0.06   0.953    -2.632114    2.479956
          935  |   .7324932    .842337     0.87   0.385    -.9184571    2.383443
          936  |          0  (empty)
          946  |   .6517971   .4381997     1.49   0.137    -.2070585    1.510653
          976  |          0  (empty)
          977  |   .0208652   .3784722     0.06   0.956    -.7209268    .7626571
          980  |   .0204106   .6374646     0.03   0.974    -1.228997    1.269818
          981  |  -1.149274   1.141717    -1.01   0.314    -3.386999    1.088451
          989  |   .2498448   .6871029     0.36   0.716    -1.096852    1.596542
          992  |  -.1352768   .7759873    -0.17   0.862    -1.656184     1.38563
               |
         _cons |  -1.325683   .4979668    -2.66   0.008     -2.30168   -.3496864
--------------------------------------------------------------------------------

. predict logit1
(option pr assumed; Pr(voted22))
(685 missing values generated)

. elasticnet logit voted22 female molincome  firstvote foreign i.moses1d i.mohighschool i.kunta19 if  ssample1==0

alpha 1 of 3: alpha = 1
note: 275.kunta19 omitted because it is constant.
note: 834.kunta19 omitted because it is constant.
note: 46.kunta19 omitted because it is constant in C.V. subsamples.
note: 72.kunta19 omitted because it is constant in C.V. subsamples.
note: 78.kunta19 omitted because it is constant in C.V. subsamples.
note: 90.kunta19 omitted because it is constant in C.V. subsamples.
note: 102.kunta19 omitted because it is constant in C.V. subsamples.
note: 105.kunta19 omitted because it is constant in C.V. subsamples.
note: 148.kunta19 omitted because it is constant in C.V. subsamples.
note: 172.kunta19 omitted because it is constant in C.V. subsamples.
note: 178.kunta19 omitted because it is constant in C.V. subsamples.
note: 218.kunta19 omitted because it is constant in C.V. subsamples.
note: 250.kunta19 omitted because it is constant in C.V. subsamples.
note: 273.kunta19 omitted because it is constant in C.V. subsamples.
note: 280.kunta19 omitted because it is constant in C.V. subsamples.
note: 288.kunta19 omitted because it is constant in C.V. subsamples.
note: 291.kunta19 omitted because it is constant in C.V. subsamples.
note: 301.kunta19 omitted because it is constant in C.V. subsamples.
note: 312.kunta19 omitted because it is constant in C.V. subsamples.
note: 317.kunta19 omitted because it is constant in C.V. subsamples.
note: 320.kunta19 omitted because it is constant in C.V. subsamples.
note: 407.kunta19 omitted because it is constant in C.V. subsamples.
note: 483.kunta19 omitted because it is constant in C.V. subsamples.
note: 484.kunta19 omitted because it is constant in C.V. subsamples.
note: 601.kunta19 omitted because it is constant in C.V. subsamples.
note: 631.kunta19 omitted because it is constant in C.V. subsamples.
note: 636.kunta19 omitted because it is constant in C.V. subsamples.
note: 689.kunta19 omitted because it is constant in C.V. subsamples.
note: 702.kunta19 omitted because it is constant in C.V. subsamples.
note: 704.kunta19 omitted because it is constant in C.V. subsamples.
note: 707.kunta19 omitted because it is constant in C.V. subsamples.
note: 748.kunta19 omitted because it is constant in C.V. subsamples.
note: 755.kunta19 omitted because it is constant in C.V. subsamples.
note: 777.kunta19 omitted because it is constant in C.V. subsamples.
note: 778.kunta19 omitted because it is constant in C.V. subsamples.
note: 783.kunta19 omitted because it is constant in C.V. subsamples.
note: 846.kunta19 omitted because it is constant in C.V. subsamples.
note: 854.kunta19 omitted because it is constant in C.V. subsamples.
note: 857.kunta19 omitted because it is constant in C.V. subsamples.
note: 922.kunta19 omitted because it is constant in C.V. subsamples.
note: 936.kunta19 omitted because it is constant in C.V. subsamples.
note: 976.kunta19 omitted because it is constant in C.V. subsamples.
10-fold cross-validation with 109 lambdas ...
Grid value 1:     lambda = .1820055   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 2:     lambda = .1658366   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 3:     lambda = .1511041   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 4:     lambda = .1376805   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 5:     lambda = .1254493   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 6:     lambda =  .121337   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 7:     lambda = .1105577   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 8:     lambda = .1007361   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 9:     lambda =  .091787   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232276
Grid value 10:    lambda = .0910027   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.231994
Grid value 11:    lambda = .0829183   no. of nonzero coef. =       1
Folds: 1...5....10   CVF = 1.225807
Grid value 12:    lambda = .0755521   no. of nonzero coef. =       1
Folds: 1...5....10   CVF = 1.220328
Grid value 13:    lambda = .0688402   no. of nonzero coef. =       1
Folds: 1...5....10   CVF = 1.215783
Grid value 14:    lambda = .0627247   no. of nonzero coef. =       1
Folds: 1...5....10   CVF = 1.212012
Grid value 15:    lambda = .0571524   no. of nonzero coef. =       1
Folds: 1...5....10   CVF = 1.208874
Grid value 16:    lambda = .0520751   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.205387
Grid value 17:    lambda = .0474489   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.201428
Grid value 18:    lambda = .0432337   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.197954
Grid value 19:    lambda = .0393929   no. of nonzero coef. =       2
Folds: 1...5....10   CVF =  1.19506
Grid value 20:    lambda = .0358934   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.192581
Grid value 21:    lambda = .0327047   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.190228
Grid value 22:    lambda = .0297993   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.188084
Grid value 23:    lambda =  .027152   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.186009
Grid value 24:    lambda = .0247399   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.183828
Grid value 25:    lambda = .0225421   no. of nonzero coef. =       7
Folds: 1...5....10   CVF =  1.18178
Grid value 26:    lambda = .0205395   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.179931
Grid value 27:    lambda = .0187148   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.178303
Grid value 28:    lambda = .0170523   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.176863
Grid value 29:    lambda = .0155374   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.175575
Grid value 30:    lambda = .0141571   no. of nonzero coef. =      12
Folds: 1...5....10   CVF = 1.174211
Grid value 31:    lambda = .0128994   no. of nonzero coef. =      13
Folds: 1...5....10   CVF = 1.172653
Grid value 32:    lambda = .0117535   no. of nonzero coef. =      14
Folds: 1...5....10   CVF =  1.17129
Grid value 33:    lambda = .0107093   no. of nonzero coef. =      18
Folds: 1...5....10   CVF =  1.17013
Grid value 34:    lambda = .0097579   no. of nonzero coef. =      23
Folds: 1...5....10   CVF = 1.169171
Grid value 35:    lambda = .0088911   no. of nonzero coef. =      27
Folds: 1...5....10   CVF = 1.168303
Grid value 36:    lambda = .0081012   no. of nonzero coef. =      35
Folds: 1...5....10   CVF = 1.167742
Grid value 37:    lambda = .0073815   no. of nonzero coef. =      45
Folds: 1...5....10   CVF = 1.167275
Grid value 38:    lambda = .0067258   no. of nonzero coef. =      49
Folds: 1...5....10   CVF = 1.166861
Grid value 39:    lambda = .0061283   no. of nonzero coef. =      55
Folds: 1...5....10   CVF = 1.166583
Grid value 40:    lambda = .0055838   no. of nonzero coef. =      63
Folds: 1...5....10   CVF = 1.166504
Grid value 41:    lambda = .0050878   no. of nonzero coef. =      73
Folds: 1...5....10   CVF = 1.166582
Grid value 42:    lambda = .0046358   no. of nonzero coef. =      81
Folds: 1...5....10   CVF = 1.166804
Grid value 43:    lambda =  .004224   no. of nonzero coef. =      92
Folds: 1...5....10   CVF = 1.167178
Grid value 44:    lambda = .0038487   no. of nonzero coef. =     103
Folds: 1...5....10   CVF = 1.167744
Grid value 45:    lambda = .0035068   no. of nonzero coef. =     113
Folds: 1...5....10   CVF = 1.168482
Grid value 46:    lambda = .0031953   no. of nonzero coef. =     124
Folds: 1...5....10   CVF = 1.169313
... cross-validation complete ... minimum found

alpha 2 of 3: alpha = 0.75
note: 275.kunta19 omitted because it is constant.
note: 834.kunta19 omitted because it is constant.
note: 46.kunta19 omitted because it is constant in C.V. subsamples.
note: 72.kunta19 omitted because it is constant in C.V. subsamples.
note: 78.kunta19 omitted because it is constant in C.V. subsamples.
note: 90.kunta19 omitted because it is constant in C.V. subsamples.
note: 102.kunta19 omitted because it is constant in C.V. subsamples.
note: 105.kunta19 omitted because it is constant in C.V. subsamples.
note: 148.kunta19 omitted because it is constant in C.V. subsamples.
note: 172.kunta19 omitted because it is constant in C.V. subsamples.
note: 178.kunta19 omitted because it is constant in C.V. subsamples.
note: 218.kunta19 omitted because it is constant in C.V. subsamples.
note: 250.kunta19 omitted because it is constant in C.V. subsamples.
note: 273.kunta19 omitted because it is constant in C.V. subsamples.
note: 280.kunta19 omitted because it is constant in C.V. subsamples.
note: 288.kunta19 omitted because it is constant in C.V. subsamples.
note: 291.kunta19 omitted because it is constant in C.V. subsamples.
note: 301.kunta19 omitted because it is constant in C.V. subsamples.
note: 312.kunta19 omitted because it is constant in C.V. subsamples.
note: 317.kunta19 omitted because it is constant in C.V. subsamples.
note: 320.kunta19 omitted because it is constant in C.V. subsamples.
note: 407.kunta19 omitted because it is constant in C.V. subsamples.
note: 483.kunta19 omitted because it is constant in C.V. subsamples.
note: 484.kunta19 omitted because it is constant in C.V. subsamples.
note: 601.kunta19 omitted because it is constant in C.V. subsamples.
note: 631.kunta19 omitted because it is constant in C.V. subsamples.
note: 636.kunta19 omitted because it is constant in C.V. subsamples.
note: 689.kunta19 omitted because it is constant in C.V. subsamples.
note: 702.kunta19 omitted because it is constant in C.V. subsamples.
note: 704.kunta19 omitted because it is constant in C.V. subsamples.
note: 707.kunta19 omitted because it is constant in C.V. subsamples.
note: 748.kunta19 omitted because it is constant in C.V. subsamples.
note: 755.kunta19 omitted because it is constant in C.V. subsamples.
note: 777.kunta19 omitted because it is constant in C.V. subsamples.
note: 778.kunta19 omitted because it is constant in C.V. subsamples.
note: 783.kunta19 omitted because it is constant in C.V. subsamples.
note: 846.kunta19 omitted because it is constant in C.V. subsamples.
note: 854.kunta19 omitted because it is constant in C.V. subsamples.
note: 857.kunta19 omitted because it is constant in C.V. subsamples.
note: 922.kunta19 omitted because it is constant in C.V. subsamples.
note: 936.kunta19 omitted because it is constant in C.V. subsamples.
note: 976.kunta19 omitted because it is constant in C.V. subsamples.
10-fold cross-validation with 109 lambdas ...
Grid value 1:     lambda = .1820055   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 2:     lambda = .1658366   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 3:     lambda = .1511041   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 4:     lambda = .1376805   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 5:     lambda = .1254493   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232332
Grid value 6:     lambda =  .121337   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232016
Grid value 7:     lambda = .1105577   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.226187
Grid value 8:     lambda = .1007361   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.220937
Grid value 9:     lambda =  .091787   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.216516
Grid value 10:    lambda = .0910027   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.216145
Grid value 11:    lambda = .0829183   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.212481
Grid value 12:    lambda = .0755521   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.209382
Grid value 13:    lambda = .0688402   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.205839
Grid value 14:    lambda = .0627247   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.201931
Grid value 15:    lambda = .0571524   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.198526
Grid value 16:    lambda = .0520751   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.195653
Grid value 17:    lambda = .0474489   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.193098
Grid value 18:    lambda = .0432337   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.190661
Grid value 19:    lambda = .0393929   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.188492
Grid value 20:    lambda = .0358934   no. of nonzero coef. =       6
Folds: 1...5....10   CVF = 1.186331
Grid value 21:    lambda = .0327047   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.184088
Grid value 22:    lambda = .0297993   no. of nonzero coef. =       8
Folds: 1...5....10   CVF =    1.182
Grid value 23:    lambda =  .027152   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.180145
Grid value 24:    lambda = .0247399   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.178504
Grid value 25:    lambda = .0225421   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.177049
Grid value 26:    lambda = .0205395   no. of nonzero coef. =      11
Folds: 1...5....10   CVF = 1.175731
Grid value 27:    lambda = .0187148   no. of nonzero coef. =      14
Folds: 1...5....10   CVF = 1.174326
Grid value 28:    lambda = .0170523   no. of nonzero coef. =      14
Folds: 1...5....10   CVF = 1.172748
Grid value 29:    lambda = .0155374   no. of nonzero coef. =      15
Folds: 1...5....10   CVF = 1.171386
Grid value 30:    lambda = .0141571   no. of nonzero coef. =      20
Folds: 1...5....10   CVF = 1.170219
Grid value 31:    lambda = .0128994   no. of nonzero coef. =      24
Folds: 1...5....10   CVF = 1.169248
Grid value 32:    lambda = .0117535   no. of nonzero coef. =      29
Folds: 1...5....10   CVF = 1.168363
Grid value 33:    lambda = .0107093   no. of nonzero coef. =      38
Folds: 1...5....10   CVF = 1.167799
Grid value 34:    lambda = .0097579   no. of nonzero coef. =      46
Folds: 1...5....10   CVF = 1.167311
Grid value 35:    lambda = .0088911   no. of nonzero coef. =      50
Folds: 1...5....10   CVF = 1.166891
Grid value 36:    lambda = .0081012   no. of nonzero coef. =      56
Folds: 1...5....10   CVF = 1.166606
Grid value 37:    lambda = .0073815   no. of nonzero coef. =      65
Folds: 1...5....10   CVF = 1.166526
Grid value 38:    lambda = .0067258   no. of nonzero coef. =      75
Folds: 1...5....10   CVF = 1.166596
Grid value 39:    lambda = .0061283   no. of nonzero coef. =      85
Folds: 1...5....10   CVF = 1.166819
Grid value 40:    lambda = .0055838   no. of nonzero coef. =      96
Folds: 1...5....10   CVF = 1.167192
Grid value 41:    lambda = .0050878   no. of nonzero coef. =     106
Folds: 1...5....10   CVF = 1.167761
Grid value 42:    lambda = .0046358   no. of nonzero coef. =     116
Folds: 1...5....10   CVF = 1.168493
Grid value 43:    lambda =  .004224   no. of nonzero coef. =     125
Folds: 1...5....10   CVF = 1.169318
... cross-validation complete ... minimum found

alpha 3 of 3: alpha = 0.5
note: 275.kunta19 omitted because it is constant.
note: 834.kunta19 omitted because it is constant.
note: 46.kunta19 omitted because it is constant in C.V. subsamples.
note: 72.kunta19 omitted because it is constant in C.V. subsamples.
note: 78.kunta19 omitted because it is constant in C.V. subsamples.
note: 90.kunta19 omitted because it is constant in C.V. subsamples.
note: 102.kunta19 omitted because it is constant in C.V. subsamples.
note: 105.kunta19 omitted because it is constant in C.V. subsamples.
note: 148.kunta19 omitted because it is constant in C.V. subsamples.
note: 172.kunta19 omitted because it is constant in C.V. subsamples.
note: 178.kunta19 omitted because it is constant in C.V. subsamples.
note: 218.kunta19 omitted because it is constant in C.V. subsamples.
note: 250.kunta19 omitted because it is constant in C.V. subsamples.
note: 273.kunta19 omitted because it is constant in C.V. subsamples.
note: 280.kunta19 omitted because it is constant in C.V. subsamples.
note: 288.kunta19 omitted because it is constant in C.V. subsamples.
note: 291.kunta19 omitted because it is constant in C.V. subsamples.
note: 301.kunta19 omitted because it is constant in C.V. subsamples.
note: 312.kunta19 omitted because it is constant in C.V. subsamples.
note: 317.kunta19 omitted because it is constant in C.V. subsamples.
note: 320.kunta19 omitted because it is constant in C.V. subsamples.
note: 407.kunta19 omitted because it is constant in C.V. subsamples.
note: 483.kunta19 omitted because it is constant in C.V. subsamples.
note: 484.kunta19 omitted because it is constant in C.V. subsamples.
note: 601.kunta19 omitted because it is constant in C.V. subsamples.
note: 631.kunta19 omitted because it is constant in C.V. subsamples.
note: 636.kunta19 omitted because it is constant in C.V. subsamples.
note: 689.kunta19 omitted because it is constant in C.V. subsamples.
note: 702.kunta19 omitted because it is constant in C.V. subsamples.
note: 704.kunta19 omitted because it is constant in C.V. subsamples.
note: 707.kunta19 omitted because it is constant in C.V. subsamples.
note: 748.kunta19 omitted because it is constant in C.V. subsamples.
note: 755.kunta19 omitted because it is constant in C.V. subsamples.
note: 777.kunta19 omitted because it is constant in C.V. subsamples.
note: 778.kunta19 omitted because it is constant in C.V. subsamples.
note: 783.kunta19 omitted because it is constant in C.V. subsamples.
note: 846.kunta19 omitted because it is constant in C.V. subsamples.
note: 854.kunta19 omitted because it is constant in C.V. subsamples.
note: 857.kunta19 omitted because it is constant in C.V. subsamples.
note: 922.kunta19 omitted because it is constant in C.V. subsamples.
note: 936.kunta19 omitted because it is constant in C.V. subsamples.
note: 976.kunta19 omitted because it is constant in C.V. subsamples.
10-fold cross-validation with 109 lambdas ...
Grid value 1:     lambda = .1820055   no. of nonzero coef. =       0
Folds: 1...5....10   CVF = 1.232053
Grid value 2:     lambda = .1658366   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.226828
Grid value 3:     lambda = .1511041   no. of nonzero coef. =       2
Folds: 1...5....10   CVF =  1.22199
Grid value 4:     lambda = .1376805   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.217805
Grid value 5:     lambda = .1254493   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.214195
Grid value 6:     lambda =  .121337   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.213027
Grid value 7:     lambda = .1105577   no. of nonzero coef. =       2
Folds: 1...5....10   CVF = 1.209987
Grid value 8:     lambda = .1007361   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.206309
Grid value 9:     lambda =  .091787   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.202555
Grid value 10:    lambda = .0910027   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.202235
Grid value 11:    lambda = .0829183   no. of nonzero coef. =       3
Folds: 1...5....10   CVF = 1.199036
Grid value 12:    lambda = .0755521   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.196217
Grid value 13:    lambda = .0688402   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.193522
Grid value 14:    lambda = .0627247   no. of nonzero coef. =       4
Folds: 1...5....10   CVF = 1.191089
Grid value 15:    lambda = .0571524   no. of nonzero coef. =       5
Folds: 1...5....10   CVF = 1.188841
Grid value 16:    lambda = .0520751   no. of nonzero coef. =       7
Folds: 1...5....10   CVF =  1.18649
Grid value 17:    lambda = .0474489   no. of nonzero coef. =       7
Folds: 1...5....10   CVF = 1.184183
Grid value 18:    lambda = .0432337   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.182115
Grid value 19:    lambda = .0393929   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.180268
Grid value 20:    lambda = .0358934   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.178639
Grid value 21:    lambda = .0327047   no. of nonzero coef. =       8
Folds: 1...5....10   CVF = 1.177186
Grid value 22:    lambda = .0297993   no. of nonzero coef. =      12
Folds: 1...5....10   CVF = 1.175795
Grid value 23:    lambda =  .027152   no. of nonzero coef. =      14
Folds: 1...5....10   CVF = 1.174279
Grid value 24:    lambda = .0247399   no. of nonzero coef. =      15
Folds: 1...5....10   CVF = 1.172704
Grid value 25:    lambda = .0225421   no. of nonzero coef. =      17
Folds: 1...5....10   CVF = 1.171362
Grid value 26:    lambda = .0205395   no. of nonzero coef. =      21
Folds: 1...5....10   CVF = 1.170222
Grid value 27:    lambda = .0187148   no. of nonzero coef. =      27
Folds: 1...5....10   CVF = 1.169243
Grid value 28:    lambda = .0170523   no. of nonzero coef. =      32
Folds: 1...5....10   CVF = 1.168378
Grid value 29:    lambda = .0155374   no. of nonzero coef. =      42
Folds: 1...5....10   CVF = 1.167817
Grid value 30:    lambda = .0141571   no. of nonzero coef. =      48
Folds: 1...5....10   CVF = 1.167289
Grid value 31:    lambda = .0128994   no. of nonzero coef. =      52
Folds: 1...5....10   CVF = 1.166897
Grid value 32:    lambda = .0117535   no. of nonzero coef. =      59
Folds: 1...5....10   CVF = 1.166635
Grid value 33:    lambda = .0107093   no. of nonzero coef. =      66
Folds: 1...5....10   CVF = 1.166574
Grid value 34:    lambda = .0097579   no. of nonzero coef. =      76
Folds: 1...5....10   CVF = 1.166666
Grid value 35:    lambda = .0088911   no. of nonzero coef. =      88
Folds: 1...5....10   CVF = 1.166908
Grid value 36:    lambda = .0081012   no. of nonzero coef. =     100
Folds: 1...5....10   CVF =  1.16731
Grid value 37:    lambda = .0073815   no. of nonzero coef. =     110
Folds: 1...5....10   CVF = 1.167924
Grid value 38:    lambda = .0067258   no. of nonzero coef. =     120
Folds: 1...5....10   CVF = 1.168647
Grid value 39:    lambda = .0061283   no. of nonzero coef. =     130
Folds: 1...5....10   CVF = 1.169493
... cross-validation complete ... minimum found

Elastic net logit model                          No. of obs        =      9,926
                                                 No. of covariates =        218
Selection: Cross-validation                      No. of CV folds   =         10

-------------------------------------------------------------------------------
               |                               No. of      Out-of-
               |                              nonzero       sample      CV mean
alpha       ID |     Description      lambda    coef.   dev. ratio     deviance
---------------+---------------------------------------------------------------
1.000          |
             1 |    first lambda    .1820055        0      -0.0001     1.232332
            39 |   lambda before    .0061283       55       0.0532     1.166583
          * 40 | selected lambda    .0055838       63       0.0533     1.166504
            41 |    lambda after    .0050878       73       0.0532     1.166582
            46 |     last lambda    .0031953      124       0.0510     1.169313
---------------+---------------------------------------------------------------
0.750          |
            47 |    first lambda    .1820055        0      -0.0001     1.232332
            89 |     last lambda     .004224      125       0.0510     1.169318
---------------+---------------------------------------------------------------
0.500          |
            90 |    first lambda    .1820055        0       0.0001     1.232053
           128 |     last lambda    .0061283      130       0.0509     1.169493
-------------------------------------------------------------------------------
* alpha and lambda selected by cross-validation.

. predict enet1
(options pr penalized assumed; Pr(voted22) with penalized coefficients)

. 
. 
. 
. 
. 
. bys shnro: gen sample_n=_n

. 
. 
. gen logit_pred=logit1 if sample_n==1
(685 missing values generated)

. 
. 
. gen enet_pred=enet1 if sample_n==1
(117 missing values generated)

. 
. 
. gen outsample=ssample1 if sample_n==1

. 
. 
. 
. 
. ****************
. 
. 
. 
. 
. 
. *ROC AUC graphs
. 
. 
. roccomp voted22 logit_pred enet_pred if outsample==1, graph plot1opts(lpattern(dash)) plot2opts(lpattern(solid))

. capture graph close

. 
. roccomp voted22 logit_pred enet_pred if outsample==0 , graph plot1opts(lpattern(dash)) plot2opts(lpattern(solid)
> )

. capture graph close

. 
. 
. 
. 
end of do-file

. 
. *Close log
. log close
      name:  <unnamed>
       log:  W:\dofiles\replication\Replication_log.log
  log type:  text
 closed on:   5 Jun 2024, 08:50:40
------------------------------------------------------------------------------------------------------------------
